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  • 1. A. Alkhamisi, Mahdi
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The Effect of Fat-Tailed Error Terms on the Properties of the Systemwise RESET Test2008In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 35, no 1, p. 101-113Article in journal (Refereed)
  • 2.
    Albing, Malin
    et al.
    Department of Mathematics, Luleå University of Technology.
    Vännman, Kerstin
    Department of Mathematics, Luleå University of Technology.
    Elliptical safety region plots for Cpk2011In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 38, no 6, p. 1169-1187Article in journal (Refereed)
    Abstract [en]

    The process capability index C pk is widely used when measuring the capability of a manufacturing process. A process is defined to be capable if the capability index exceeds a stated threshold value, e.g. C pk >4/3. This inequality can be expressed graphically using a process capability plot, which is a plot in the plane defined by the process mean and the process standard deviation, showing the region for a capable process. In the process capability plot, a safety region can be plotted to obtain a simple graphical decision rule to assess process capability at a given significance level. We consider safety regions to be used for the index C pk . Under the assumption of normality, we derive elliptical safety regions so that, using a random sample, conclusions about the process capability can be drawn at a given significance level. This simple graphical tool is helpful when trying to understand whether it is the variability, the deviation from target, or both that need to be reduced to improve the capability. Furthermore, using safety regions, several characteristics with different specification limits and different sample sizes can be monitored in the same plot. The proposed graphical decision rule is also investigated with respect to power.

  • 3. Albing, Malin
    et al.
    Vännman, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Elliptical safety region plots for Cpk2011In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 38, no 6, p. 1169-1187Article in journal (Refereed)
    Abstract [en]

    The process capability index Cpk is widely used when measuring the capability of a manufacturing process. A process is defined to be capable if the capability index exceeds a stated threshold value, e.g. Cpk4/3. This inequality can be expressed graphically using a process capability plot, which is a plot in the plane defined by the process mean and the process standard deviation, showing the region for a capable process. In the process capability plot, a safety region can be plotted to obtain a simple graphical decision rule to assess process capability at a given significance level. We consider safety regions to be used for the index Cpk. Under the assumption of normality, we derive elliptical safety regions so that, using a random sample, conclusions about the process capability can be drawn at a given significance level. This simple graphical tool is helpful when trying to understand whether it is the variability, the deviation from target, or both that need to be reduced to improve the capability. Furthermore, using safety regions, several characteristics with different specification limits and different sample sizes can be monitored in the same plot. The proposed graphical decision rule is also investigated with respect to power

  • 4. Albing, Malin
    et al.
    Vännman, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Skewed zero-bound distributions and process capability indices for upper specifications2009In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 36, no 2, p. 205-221Article in journal (Refereed)
    Abstract [en]

    A common practical situation in process capability analysis, which is not well developed theoretically, is when the quality characteristic of interest has a skewed distribution with a long tail towards relatively large values and an upper specification limit only exists. In such situations it is not uncommon that the smallest possible value of the characteristic is 0 and this also is the best value to obtain. Hence a target value 0 is assumed to exist. We investigate a new class of process capability indices for this situation. Two estimators of the proposed index are studied and the asymptotic distributions of these estimators are derived. Furthermore we suggest a decision procedure useful when drawing conclusions about the capability at a given significance level, based on the estimated indices and their asymptotic distributions. A simulation study is also performed, assuming that the quality characteristic is Weibull distributed, to investigate the true significance level when the sample size is finite.

  • 5. Alkamisi, M. A.
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Bayesian Analysis of a Linear Mixed Model with AR(p) errors Via MCMC2005In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 32, no 7, p. 741-755Article in journal (Refereed)
  • 6. Almasri, Abdullah
    et al.
    Locking, Håkan
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Testing for climate warming in Sweden during 1850-1999, using wavelets analysis2008In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 35, no 4, p. 431-443Article in journal (Refereed)
  • 7. Arvidsson, M
    et al.
    Kammerlind, Peter
    Linköping University, The Institute of Technology. Linköping University, Department of Management and Engineering, Quality Technology and Management .
    Hynen, A
    Chalmers Univ Technol, Dept Total Qual Management, S-41296 Gothenburg, Sweden Linkoping Univ, S-58183 Linkoping, Sweden ABB Corp Res, Baden, Switzerland.
    Bergman, B
    Chalmers Univ Technol, Dept Total Qual Management, S-41296 Gothenburg, Sweden Linkoping Univ, S-58183 Linkoping, Sweden ABB Corp Res, Baden, Switzerland.
    Identification of factors influencing dispersion in split-plot experiments2001In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 28, no 3-4, p. 269-283Article in journal (Refereed)
    Abstract [en]

    As split-plot designs are commonly used in robust design it is important to identify factors in these designs that influence the dispersion of the response variable. In this article, the Bergman-Hynen method, developed for identification of dispersion effects in unreplicated experiments, is modified to be used in the context of split-plot experiments. The modification of the Bergman-Hynen method enables identification of factors that influence specific variance components in unreplicated two-level fractional factorial split-plot experiments. An industrial example is used to illustrate the proposed method.

  • 8.
    Bayisa, Fekadu
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Model-based computed tomography image estimation: partitioning approach2019In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    There is a growing interest to get a fully MR based radiotherapy. The most important development needed is to obtain improved bone tissue estimation. The existing model-based methods perform poorly on bone tissues. This paper was aimed at obtaining improved bone tissue estimation. Skew-Gaussian mixture model and Gaussian mixture model were proposed to investigate CT image estimation from MR images by partitioning the data into two major tissue types. The performance of the proposed models was evaluated using the leaveone-out cross-validation method on real data. In comparison with the existing model-based approaches, the model-based partitioning approach outperformed in bone tissue estimation, especially in dense bone tissue estimation.

  • 9.
    Berhe, Leakemariam
    et al.
    Wondo Genet College of Forestry and Natural Resources, Hawassa University, Awassa, Ethiopia.
    Arnoldsson, Göran
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Ds-optimal designs for Kozak's tree taper model2011In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 38, no 5, p. 1087-1102Article in journal (Refereed)
    Abstract [en]

    In this work, we study Ds-optimal design for Kozak's tree taper model. The approximate Ds-optimal designs are found invariant to tree size and hence create a ground to construct a general replication-free Ds-optimal design. Even though the designs are found not to be dependent on the parameter value p of the Kozak's model, they are sensitive to the stimes1 subset parameter vector values of the model. The 12 points replication-free design (with 91% efficiency) suggested in this study is believed to reduce cost and time for data collection and more importantly to precisely estimate the subset parameters of interest.

  • 10.
    Ciavolino, E.
    et al.
    Dipartimento di Filosofia e Scienze Sociali, Università del Salento, Lecce, Italy.
    Jörn Dahlgaard, Jens
    Linköping University, Department of Management and Engineering, Quality Technology and Management . Linköping University, The Institute of Technology.
    Simultaneous Equation Model based on the generalized maximum entropy for studying the effect of management factors on enterprise performance2009In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 36, no 7, p. 801-815Article in journal (Refereed)
    Abstract [en]

    The aim of this paper is to study the effect of management factors on enterprise performance, considering a survey that the University Consortium in Engineering for Quality and Innovation has led. The relationships between management factors and enterprise performance are formalized by a Simultaneous Equation Model based on the generalized maximum entropy (GME) estimation method. The format of this paper is as follows. In Section 2, the data collected, the questionnaire evaluation, and the management model analytical formulation are introduced. In Section 3, the GME formulation is specified, showing the main characteristics of the estimation method. In Section 4, the results and a comparison among GME, partial least squares (PLS), and maximum likelihood estimation (MLE) is shown. In Section 5, concluding remarks are discussed.

  • 11.
    Fackle-Fornius, Ellinor
    et al.
    Stockholm University, Sweden .
    Wänström, Linda
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, The Institute of Technology.
    Minimax D-optimal designs of contingent valuation experiments: willingness to pay for environmentally friendly clothes2014In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 41, no 4, p. 895-908Article in journal (Refereed)
    Abstract [en]

    This paper demonstrates how to plan a contingent valuation experiment to assess the value of ecologically produced clothes. First, an appropriate statistical model (the trinomial spike model) that describes the probability that a randomly selected individual will accept any positive bid, and if so, will accept the bid A, is defined. Secondly, an optimization criterion that is a function of the variances of the parameter estimators is chosen. However, the variances of the parameter estimators in this model depend on the true parameter values. Pilot study data are therefore used to obtain estimates of the parameter values and a locally optimal design is found. Because this design is only optimal given that the estimated parameter values are correct, a design that minimizes the maximum of the criterion function over a plausable parameter region (i.e. a minimax design) is then found.

  • 12.
    Fackle-Fornius, Ellinor
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Wänström, Linda
    Minimax D-optimal designs of contingent valuation experiments: willingness to pay for environmentally friendly clothes2014In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 41, no 4, p. 895-908Article in journal (Refereed)
    Abstract [en]

    This paper demonstrates how to plan a contingent valuation experiment to assess the value of ecologically produced clothes. First, an appropriate statistical model (the trinomial spike model) that describes the probability that a randomly selected individual will accept any positive bid, and if so, will accept the bid A, is defined. Secondly, an optimization criterion that is a function of the variances of the parameter estimators is chosen. However, the variances of the parameter estimators in this model depend on the true parameter values. Pilot study data are therefore used to obtain estimates of the parameter values and a locally optimal design is found. Because this design is only optimal given that the estimated parameter values are correct, a design that minimizes the maximum of the criterion function over a plausable parameter region (i.e. a minimax design) is then found.

  • 13.
    Ghilagaber, Gebrenegus
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Munezero, Parfait
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Bayesian Change-point Modelling of the Effects of 3-points-for-a-win Rule in Football2019In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532Article in journal (Refereed)
    Abstract [en]

    We examine the effects of the 3-points-for-a-win (3pfaw) rule in the football world. Data that form the basis of our analyses come from seven leagues around the world (Albania, Brazil, England, Germany, Poland, Romania, and Scotland) and consist of mean goals and proportions of decided matches over a period of about six years before- and about seven years after the introduction of the rule in the respective leagues. Bayesian change-point analyses and Shiryaev-Roberts tests show that the rule had no effects on the mean goals but, indeed, had increasing effects on the proportions of decided matches in most of the leagues studied. This, in turn, implies that while the rule has given teams the incentive to aim at winning matches, such aim was not achieved by scoring excess goals. Instead, it was achieved by scoring enough goals in order to win and, at the same time, defending enough in order not to lose. Our results are in accordance with recent findings on comparing the values of attack and defense - that, in top-level football, not conceding a goal is more valuable than scoring a single goal.

  • 14.
    Hacker, R Scott
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Hatemi-J, Abdulnasser
    Optimal Lag Length Choice in Stable and Unstable VAR Models under Situations of Homoscedasticity and ARCH2008In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 35, no 6, p. 601-615Article in journal (Refereed)
    Abstract [en]

    The performance of different information criteria - namely Akaike, corrected Akaike (AICC), Schwarz-Bayesian (SBC), and Hannan-Quinn - is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.

  • 15.
    Hacker, R. Scott
    et al.
    Jönköping Int Business Sch, Dept Econ, Jönköping, Sweden.
    Hatemi-J., Abdulnasser
    University of Skövde, School of Technology and Society.
    Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH2008In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 35, no 6, p. 601-615Article in journal (Refereed)
    Abstract [en]

    The performance of different information criteria - namely Akaike, corrected Akaike (AICC), Schwarz-Bayesian (SBC), and Hannan-Quinn - is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.

  • 16.
    Holgersson, Thomas
    Högskolan i Jönköping.
    Testing for Multivariate Autocorrelation2004In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 31, no 4, p. 379-395Article in journal (Refereed)
    Abstract [en]

    This paper concerns the problem of assessing autocorrelation of multivariate (i.e. systemwise) models. It is well known that systemwise diagnostic tests for autocorrelation often suffers from poor small sample properties in the sense that the true size overstates the nominal size. The failure of keeping control of the size usually stems from the fact that the critical values (used to decide the rejection area) originate from the slowly converging asymptotic null distribution. Another drawback of existing tests is that the power may be rather low if the deviation from the null is not symmetrical over the marginal models. In this paper we consider four quite different test techniques for autocorrelation. These are (i) Pillai's trace, (ii) Roy's largest root, (iii) the maximum F-statistic and (iv) the maximum t2 test. We show how to obtain control of the size of the tests, and then examine the true (small sample) size and power properties by means of Monte Carlo simulations.

  • 17.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Testing for Multivariate Autocorrelation2004In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 31, no 4, p. 379-395Article in journal (Refereed)
  • 18.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Karlsson, Peter
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Three estimators of the Mahalanobis distance in high-dimensional data2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 12, p. 2713-2720Article in journal (Refereed)
    Abstract [en]

    This paper treats the problem of estimating the Mahalanobis distance for the purpose of detecting outliersin high-dimensional data. Three ridge-type estimators are proposed and risk functions for deciding anappropriate value of the ridge coefficient are developed. It is argued that one of the ridge estimator hasparticularly tractable properties, which is demonstrated through outlier analysis of real and simulated data.

  • 19.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Karlsson, Peter
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Mansoor, Rashid
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Estimating mean-standard deviation ratios of financial data2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 3, p. 657-671Article in journal (Refereed)
    Abstract [en]

    This article treats the problem of linking the relation between excess return and risk of financial assets when the returns follow a factor structure. The authors propose three different estimators and their consistencies are established in cases when the number of assets in the cross-section (n) and the number of observations over time (T) are of comparable size. An empirical investigation is conducted on the Stockholm stock exchange market where the mean-standard deviation ratio is calculated for small- mid- and large cap segments, respectively.

  • 20.
    Holgersson, Thomas
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Karlsson, Peter S.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Three estimators of the Mahalanobis distance in high-dimensional data2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 12, p. 2713-2720Article in journal (Refereed)
    Abstract [en]

    This paper treats the problem of estimating the Mahalanobis distance for the purpose of detecting outliers in high-dimensional data. Three ridge-type estimators are proposed and risk functions for deciding an appropriate value of the ridge coefficient are developed. It is argued that one of the ridge estimator has particularly tractable properties, which is demonstrated through outlier analysis of real and simulated data.

  • 21.
    Holgersson, Thomas
    et al.
    Jönköping University.
    Karlsson, Peter S.
    Jönköping University.
    Mansoor, Rashid
    Jönköping University.
    Estimating mean-standard deviation ratios of financial data2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 3, p. 657-671Article in journal (Refereed)
    Abstract [en]

    This article treats the problem of linking the relation between excess return and risk of financial assets when the returns follow a factor structure. The authors propose three different estimators and their consistencies are established in cases when the number of assets in the cross-section (n) and the number of observations over time (T) are of comparable size. An empirical investigation is conducted on the Stockholm stock exchange market where the mean-standard deviation ratio is calculated for small- mid- and large cap segments, respectively.

  • 22.
    Holgersson, Thomas
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    Nordström, Louise
    Jönköping University.
    Öner, Özge
    Jönköping University.
    Dummy Variables vs. Category-wise Models2014In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 41, no 2, p. 233-241Article in journal (Refereed)
    Abstract [en]

    Empirical research frequently involves regression analysis with binary categorical variables, which are traditionally handled through dummy explanatory variables. This paper argues that separate category-wise models may provide a more logical and comprehensive tool for analysing data with binary categories. Exploring different aspects of both methods, we contrast the two with a Monte Carlo simulation and an empirical example to provide a practical insight.

  • 23.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Nordström, Louise
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Öner, Özge
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Dummy variables vs. category-wise models2014In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 41, no 2, p. 233-241Article in journal (Refereed)
    Abstract [en]

    Empirical research frequently involves regression analysis with binary categorical variables, which are traditionally handled through dummy explanatory variables. This paper argues that separate category-wise models may provide a more logical and comprehensive tool for analysing data with binary categories. Exploring different aspects of both methods, we contrast the two with a Monte Carlo simulation and an empirical example to provide a practical insight.

  • 24.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Nordström, Louise
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Öner, Özge
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    On regression modelling with dummy variables versus separate regressions per group: comment on Holgersson et al.2016In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 43, no 8, p. 1564-1565Article in journal (Other academic)
  • 25.
    Holgersson, Thomas
    et al.
    Jönköping University.
    Öner, Özge
    Jönköping University.
    Nordström, Louise
    Jönköping University.
    On regression modelling with dummy variables versus separate regressions per group: comment on Holgersson et al.2016In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 43, no 8, p. 1564-1565Article in journal (Refereed)
  • 26. Hussain, Shakir
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Estimation and Forecasting of Hospital Admission Due to Influenza: Planning for Winter Pressure. The Case of the West Midlands, UK2005In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 32, no 3, p. 191-205Article in journal (Refereed)
  • 27.
    Johansson, Jan-Olof
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Modelling the surface structure of newsprint2000In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 27, no 4, p. 425-438Article in journal (Refereed)
    Abstract [en]

    The Gibbs distribution is often used to model micro-textures. This includes a definition of a neighbourhood system. If a micro-texture contains a large-scale variation, the neighbourhood system will be large, which implies many parameters in the corresponding Gibbs distribution. The estimation of the parameters for such models will be difficult and time consuming. I suggest, in this paper, a separation of the micro-texture into a large-scale variation and a small-scale variation and model each source of variation with a Gibbs distribution. This method is applied on full-tone print of newsprint to model the variation caused by print mottle. In this application, the large-scale variation is mainly caused by fibre flocculation and clustering and the small-scale variation contains the variation of the fibres and fines on and between the clusters. The separate description of these two variations makes it possible to relate different kinds of paper qualities to the appropriate source of variation.

  • 28. Kibria, B. M. Golam
    et al.
    Månsson, Kristofer
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Some Ridge Regression estimator for the zero-inflated Poisson model2013In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 40, no 4, p. 721-735Article in journal (Refereed)
    Abstract [en]

    The zero inflated Poisson regression model is very common when analysing economic data that comes in the form of non-negative integers since it accounts for excess zeros and overdispersion of the dependent variable. However, a problem often encountered when analyzing economic data that has not been addressed for this model is multicollinearity. This paper proposes ridge regression estimators and some methods of estimating the ridge parameter k for the non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both mean squared error (MSE) and mean absolute error (MAE) are considered as performance criterion. The simulation study shows that some estimators are better than the commonly used maximum likelihood estimator and some other ridge regression estimators. Based on the simulation study and an empirical application, some useful estimators are recommended for the practitioners.

  • 29.
    Kibria, B. M. Golam
    et al.
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Some ridge regression estimators for the zero-inflated Poisson model2013In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 40, no 4, p. 721-735Article in journal (Refereed)
    Abstract [en]

    The zero-inflated Poisson regression model is commonly used when analyzing economic data that come in the form of non-negative integers since it accounts for excess zeros and overdispersion of the dependent variable. However, a problem often encountered when analyzing economic data that has not been addressed for this model is multicollinearity. This paper proposes ridge regression (RR) estimators and some methods for estimating the ridge parameter k for a non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both mean squared error and mean absolute error are considered as the performance criteria. The simulation study shows that some estimators are better than the commonly used maximum-likelihood estimator and some other RR estimators. Based on the simulation study and an empirical application, some useful estimators are recommended for practitioners.

  • 30.
    Kulahci, Murat
    et al.
    Department of Industrial Engineering, Arizona State University, Tempe.
    Bisgaard, Søren
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    A generalization of the alias matrix2006In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 33, no 4, p. 387-395Article in journal (Refereed)
    Abstract [en]

    The investigation of aliases or biases is important for the interpretation of the results from factorial experiments. For two-level fractional factorials this can be facilitated through their group structure. For more general arrays the alias matrix can be used. This tool is traditionally based on the assumption that the error structure is that associated with ordinary least squares. For situations where that is not the case, we provide in this article a generalization of the alias matrix applicable under the generalized least squares assumptions. We also show that for the special case of split plot error structure, the generalized alias matrix simplifies to the ordinary alias matrix

  • 31.
    Kulahci, Murat
    et al.
    University of Wisconsin.
    Bisgaard, Søren
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst, University of Amsterdam.
    Switching-one-column follow-up experiments for Plackett-Burman designs2001In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 28, no 8, p. 943-949Article in journal (Refereed)
    Abstract [en]

    Industrial experiments are frequently performed sequentially using two-level fractional factorial designs. In this context, a common strategy for the design of follow-up experiments is to switch the signs in one column. It is well known that this strategy, when applied to two-level fractional factorial resolution III designs, will clear the main effect, for which the switch was performed, from any confounding with any other two-factor interactions and will also clear all the two-factor interactions between that factor and the other main effects from any confounding with other two-factor interactions. In this article, we extend this result and show that this strategy applies to any orthogonal two-level resolution III design and therefore specifically to any two-level Plackett-Burman design.

  • 32.
    Kulahci, Murat
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Tyssedal, John Sølve
    Department of Mathematical Sciences, The Norwegian University of Science and Technology, Trondheim.
    Split-plot designs for multistage experimentation2017In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 44, no 3, p. 493-510Article in journal (Refereed)
    Abstract [en]

    Most of today’s complex systems and processes involve several stages through which input or the raw material has to go before the final product is obtained. Also in many cases factors at different stages interact. Therefore, a holistic approach for experimentation that considers all stages at the same time will be more efficient. However, there have been only a few attempts in the literature to provide an adequate and easy-to-use approach for this problem. In this paper, we present a novel methodology for constructing two-level split-plot and multistage experiments. The methodology is based on the Kronecker product representation of orthogonal designs and can be used for any number of stages, for various numbers of subplots and for different number of subplots for each stage. The procedure is demonstrated on both regular and nonregular designs and provides the maximum number of factors that can be accommodated in each stage. Furthermore, split-plot designs for multistage experiments with good projective properties are also provided.

  • 33.
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Scale-Space Theory: A Basic Tool for Analysing Structures at Different Scales1994In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 21, p. 225-270Article in journal (Refereed)
    Abstract [en]

    An inherent property of objects in the world is that they only exist as meaningful entities over certain ranges of scale. If one aims at describing the structure of unknown real-world signals, then a multi-scale representation of data is of crucial importance.

    This article gives a tutorial review of a special type of multi-scale representation, linear scale-space representation, which has been developed by the computer vision community in order to handle image structures at different scales in a consistent manner. The basic idea is to embed the original signal into a one-parameter family of gradually smoothed signals, in which the fine scale details are successively suppressed.

    Under rather general conditions on the type of computations that are to performed at the first stages of visual processing, in what can be termed the visual front end, it can be shown that the Gaussian kernel and its derivatives are singled out as the only possible smoothing kernels. The conditions that specify the Gaussian kernel are, basically, linearity and shift-invariance combined with different ways of formalizing the notion that structures at coarse scales should correspond to simplifications of corresponding structures at fine scales --- they should not be accidental phenomena created by the smoothing method. Notably, several different ways of choosing scale-space axioms give rise to the same conclusion.

    The output from the scale-space representation can be used for a variety of early visual tasks; operations like feature detection, feature classification and shape computation can be expressed directly in terms of (possibly non-linear) combinations of Gaussian derivatives at multiple scales. In this sense, the scale-space representation can serve as a basis for early vision.

    During the last few decades a number of other approaches to multi-scale representations have been developed, which are more or less related to scale-space theory, notably the theories of pyramids, wavelets and multi-grid methods. Despite their qualitative differences, the increasing popularity of each of these approaches indicates that the crucial notion of scaleis increasingly appreciated by the computer vision community and by researchers in other related fields.

    An interesting similarity with biological vision is that the scale-space operators closely resemble receptive field profiles registered in neurophysiological studies of the mammalian retina and visual cortex.

  • 34.
    Lindström, Fredrik
    et al.
    Försäkringskassan.
    Holgersson, Thomas
    Högskolan i Jönköping.
    Forecast mean squared error reductionin the VAR(1) process2009In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 36, no 12, p. 1369-1384Article in journal (Refereed)
    Abstract [en]

    When VAR models are used to predict future outcomes, the forecast error can be substantial. Through imposition of restrictions on the off-diagonal elements of the parameter matrix, however, the information in the process may be condensed to the marginal processes. In particular, if the cross-autocorrelations in the system are small and only a small sample is available, then such a restriction may reduce the forecast mean squared error considerably.

    In this paper, we propose three different techniques to decide whether to use the restricted or unrestricted model, i.e. the full VAR(1) model or only marginal AR(1) models. In a Monte Carlo simulation study, all three proposed tests have been found to behave quite differently depending on the parameter setting. One of the proposed tests stands out, however, as the preferred one and is shown to outperform other estimators for a wide range of parameter settings.

  • 35.
    Lindström, Fredrik
    et al.
    Försäkringskassan.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Forecast mean squared error reductionin the VAR(1) process2009In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 36, no 12, p. 1369-1384Article in journal (Refereed)
  • 36.
    Mantalos, Panagiotis
    et al.
    Department of Statistics, Lund University, Lund, Sweden.
    Shukur, Ghazi
    Department of Economics, Jönköping International Business School, Jönköping University, Jönköping, Sweden.
    The effect of spillover on the Granger causality test2010In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 37, no 9, p. 1473-1486Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the properties of the Granger causality test in stationary and stable vector autoregressive models under the presence of spillover effects, that is, causality in variance. The Wald test and the WW test (the Wald test with White's proposed heteroskedasticity-consistent covariance matrix estimator imposed) are analyzed. The investigation is undertaken by using Monte Carlo simulation in which two different sample sizes and six different kinds of data-generating processes are used. The results show that the Wald test over-rejects the null hypothesis both with and without the spillover effect, and that the over-rejection in the latter case is more severe in larger samples. The size properties of the WW test are satisfactory when there is spillover between the variables. Only when there is feedback in the variance is the size of the WW test slightly affected. The Wald test is shown to have higher power than the WW test when the errors follow a GARCH(1,1) process without a spillover effect. When there is a spillover, the power of both tests deteriorates, which implies that the spillover has a negative effect on the causality tests.

  • 37.
    Mantalos, Panagiotis
    et al.
    Lund University.
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    The Effect of the Spillover on the Granger Causality Test 2010In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 37, no 9, p. 1473-1486Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the properties of the Granger causality test in stationary and stable vector autoregressive models under the presence of spillover effects, i.e., causality in variance. The Wald test and the WW test (the Wald test with White’s proposed heteroskedasticity-consistent covariance matrix estimator imposed) are analyzed. The investigation is undertaken by using Monte Carlo simulation in which two different sample sizes and six different kinds of data generating process are used. The results show that the Wald test overrejects the null hypothesis both with and without the spillover effect, and that the overrejection in the latter case is more severe in larger samples. The size properties of the WW test are satisfactory when there is spillover between the variables. Only when there is feedback in the variance is the size of the WW test slightly affected. The Wald test is shown to have higher power than the WW test when the errors follow a GARCH(1,1) process without a spillover effect. When there is spillover, the power of both tests deteriorates, which implies that the spillover has a negative effect on the causality tests.

  • 38.
    Mark, Sigyn
    et al.
    University West, Department of Engineering Science, Division of Land Surveying and Mathematics.
    Holm, Sture
    Göteborg University, Department of Mathematics.
    Test and prediction in factorial models with independent variance estimates2008In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 35, no 7, p. 773-782Article in journal (Refereed)
  • 39.
    Mukherjee, Amitava
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Purkait, Barendra
    Simultaneous semi-sequential testing of dual alternatives for pattern recognition2011In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 38, no 2, p. 399-419Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a new nonparametric simultaneous test for dual alternatives. Simultaneous tests for dual alternatives are used for pattern detection of arsenic contamination level in ground water. We consider two possible patterns, namely, monotone shift and an umbrella-type location alternative, as the dual alternatives. Pattern recognition problems of this nature are addressed in Bandyopadhyay et al. [5], stretching the idea of multiple hypotheses tests as in Benjamini and Hochberg [6]. In the present context, we develop an alternative approach based on contrasts that helps us to detect three underlying pattern much more efficiently. We illustrate the new methodology through a motivating example related to highly sensitive issue of arsenic contamination in ground water. We provide some Monte-Carlo studies related to the proposed technique and give a comparative study between different detection procedures. We also obtain some related asymptotic results.

  • 40.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Florida International University.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A restricted Liu estimator for binary regression models and its application to an applied demand system2016In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 43, no 6, p. 1119-1127Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a restricted Liu regression estimator (RLRE) for estimating the parameter vector, β, in the presence of multicollinearity, when the dependent variable is binary and it is suspected that β may belong to a linear subspace defined by =r. First, we investigate the mean squared error (MSE) properties of the new estimator and compare them with those of the restricted maximum likelihood estimator (RMLE). Then we suggest some estimators of the shrinkage parameter, and a simulation study is conducted to compare the performance of the different estimators. Finally, we show the benefit of using RLRE instead of RMLE when estimating how changes in price affect consumer demand for a specific product.

  • 41.
    Månsson, Kristofer
    et al.
    Jönköping University.
    Kibria, B.M. Golam
    Florida International University, USA.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    A restricted Liu estimator for binary regression models and its application to an applied demand system2016In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 43, no 6, p. 1119-1127Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a restricted Liu regression estimator (RLRE) for estimating the parameter vector, β, in the presence of multicollinearity, when the dependent variable is binary and it is suspected that β may belong to a linear subspace defined by =r. First, we investigate the mean squared error (MSE) properties of the new estimator and compare them with those of the restricted maximum likelihood estimator (RMLE). Then we suggest some estimators of the shrinkage parameter, and a simulation study is conducted to compare the performance of the different estimators. Finally, we show the benefit of using RLRE instead of RMLE when estimating how changes in price affect consumer demand for a specific product.

  • 42. Pavlenko, Tatjana
    et al.
    Björkström, Anders
    Stockholm University, Faculty of Science, Department of Mathematics.
    Tillander, Annika
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Covariance structure approximation via glasso in high dimensional supervised classification2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 8, p. 1643-1666Article in journal (Refereed)
    Abstract [en]

    Recent work has shown that the Lasso-based regularization is very useful for estimating the high-dimensional inverse covariance matrix. A particularly useful scheme is based on penalizing the l(1) norm of the off-diagonal elements to encourage sparsity. We embed this type of regularization into high-dimensional classification. A two-stage estimation procedure is proposed which first recovers structural zeros of the inverse covariance matrix and then enforces block sparsity by moving non-zeros closer to the main diagonal. We show that the block-diagonal approximation of the inverse covariance matrix leads to an additive classifier, and demonstrate that accounting for the structure can yield better performance accuracy. Effect of the block size on classification is explored, and a class of as ymptotically equivalent structure approximations in a high-dimensional setting is specified. We suggest a variable selection at the block level and investigate properties of this procedure in growing dimension asymptotics. We present a consistency result on the feature selection procedure, establish asymptotic lower an upper bounds for the fraction of separative blocks and specify constraints under which the reliable classification with block-wise feature selection can be performed. The relevance and benefits of the proposed approach are illustrated on both simulated and real data.

  • 43.
    Pavlenko, Tatjana
    et al.
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Björkström, Anders
    Stockholm Univ, Stockholm, Sweden.
    Tillander, Annika
    Stockholm Univ, Stockholm, Sweden.
    Covariance structure approximation via gLasso in high-dimensional supervised classification2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 8, p. 1643-1666Article in journal (Refereed)
    Abstract [en]

    Recent work has shown that the Lasso-based regularization is very useful for estimating the high-dimensional inverse covariance matrix. A particularly useful scheme is based on penalizing the l(1) norm of the off-diagonal elements to encourage sparsity. We embed this type of regularization into high-dimensional classification. A two-stage estimation procedure is proposed which first recovers structural zeros of the inverse covariance matrix and then enforces block sparsity by moving non-zeros closer to the main diagonal. We show that the block-diagonal approximation of the inverse covariance matrix leads to an additive classifier, and demonstrate that accounting for the structure can yield better performance accuracy. Effect of the block size on classification is explored, and a class of as ymptotically equivalent structure approximations in a high-dimensional setting is specified. We suggest a variable selection at the block level and investigate properties of this procedure in growing dimension asymptotics. We present a consistency result on the feature selection procedure, establish asymptotic lower an upper bounds for the fraction of separative blocks and specify constraints under which the reliable classification with block-wise feature selection can be performed. The relevance and benefits of the proposed approach are illustrated on both simulated and real data.

  • 44.
    Rydén, Jesper
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.
    Alm, Sven Erick
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.
    The effect of interaction and rounding error in two-way ANOVA: example of impact on testing for normality2010In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 37, no 10, p. 1695-1701Article in journal (Refereed)
    Abstract [en]

    A key issue in various applications of analysis of variance (ANOVA) is testing for the interaction and the interpretation of resulting ANOVA tables. In this note it is demonstrated that for a two-way ANOVA, whether interactions are incorporated or not may have a dramatic influence when considering the usual statistical tests for normality of residuals. The effect of numerical rounding is also discussed.

  • 45.
    Segerstedt, Bo
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Nyquist, Hans
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    On the conditioning problem in generalized linear models1992In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 19, no 4, p. 513-526Article in journal (Refereed)
    Abstract [en]

    When weights are assigned to a data matrix, as in the iterative least squares estimator of a generalized linear model, the condition of the data matrix is changed. In this paper a geometrical approach to studying the mechanisms which determine the changed condition is introduced. Specifically, it is found that in some cases strong multicollinearities can be weakened or eliminated by the weights while in other cases the weights can induce an ill-conditioning.

  • 46.
    Shukur, Ghazi
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Alkhamisi, Mahdi A.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Bayesian Analysis of a Linear Mixed  Model with AR(p) errors Via MCMC2005In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 32, no 7, p. 741-755Article in journal (Refereed)
  • 47.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Almasri, Abdullah
    An Illustration of the Causality Relation between Government Spending and Revenue Using Wavelet Analysis on Finnish Data2003In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 30, no 5, p. 571-584Article in journal (Refereed)
  • 48.
    Shukur, Ghazi
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Almasri, Abdullah
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    An Illustration of the Causality Relation between Government Spending and Revenue Using Wavelets Analysis on Finnish Data2003In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 30, p. 571-584Article in journal (Refereed)
  • 49.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Hatemi-J, Abdulanaser
    Multivariate-based causality tests of twin deficits in the US2002In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 29, no 6, p. 817-824Article in journal (Refereed)
  • 50.
    Shukur, Ghazi
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Hatemi-J, Abdulnasser
    A Multivariate Based Causality Test of Twin Deficits in US2002In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 29, p. 817-824Article in journal (Refereed)
12 1 - 50 of 65
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