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  • 1. A. Alkhamisi, Mahdi
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics. Statistik.
    A Monte Carlo Study of Recent Ridge Parameters2007In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 36, no 3, p. 535-547Article in journal (Refereed)
  • 2. Ahmed, S. Ejaz
    et al.
    Fallahpour, Saber
    von Rosen, Dietrich
    von Rosen, Tatjana
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Estimation of Several Intraclass Correlation Coefficients2015In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 44, no 9, p. 2315-2328Article in journal (Refereed)
    Abstract [en]

    An intraclass correlation coefficient observed in several populations is estimated. The basis is a variance-stabilizing transformation. It is shown that the intraclass correlation coefficient from any elliptical distribution should be transformed in the same way. Four estimators are compared. An estimator where the components in a vector consisting of the transformed intraclass correlation coefficients are estimated separately, an estimator based on a weighted average of these components, a pretest estimator where the equality of the components is tested and then the outcome of the test is used in the estimation procedure, and a James-Stein estimator which shrinks toward the mean.

  • 3.
    Almasri, Abdullah
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Tests for Trend: a Simulation Study2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 3, p. 598-611Article in journal (Refereed)
    Abstract [en]

    In this study, we use the wavelet analysis to construct a test statistic to test for the existence of a trend in the series. We also propose a new approach for testing the presence of trend based on the periodogram of the data. Since we are also interested in the presence of a long-memory process among the data, we study the properties of our test statistics under different degrees of dependency. We compare the results when using the band periodogram test and the wavelet test with results obtained by applying the ordinary least squares (OLS) method under the same conditions.

  • 4.
    Amiri, Saeid
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.
    Zwanzig, Silvelyn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.
    An Improvement of the Nonparametric Bootstrap Test for the Comparison of the Coefficient of Variations2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 9, p. 1726-1734Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a new test for examining the equality of the coefficient of variation between two different populations. The proposed test is based on the nonparametric bootstrap method. It appears to yield several appreciable advantages over the current tests. The quick and easy implementation of the test can be considered as advantages of the proposed test. The test is examined by the Monte Carlo simulations, and also evaluated using various numerical studies.

  • 5.
    Angelov, Nikolay
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Economics.
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Information Science, Statistics.
    Testing for unit root against stationarity using the likelihood ratio test2007In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 36, no 2, p. 391-412Article in journal (Refereed)
    Abstract [en]

    In a first order autoregressive model with drift, we derive the likelihood ratio test for a unit root against the stationary alternative. We also derive the test in a state space model with trend. Finite sample and asymptotic critical values are obtained by Monte Carlo simulations. A simulation study investigates the power performance of the likelihood ratio test and we also examine how a bias correction of the test affects the results.

  • 6. Asmussen, Sören
    et al.
    Rydén, Tobias
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    A Note on Skewness in Regenerative Simulation2011In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 40, no 1, p. 45-57Article in journal (Refereed)
    Abstract [en]

    The purpose of this article is to show, empirically and theoretically, that performance evaluation by means of regenerative simulation often involves random variables with distributions that are heavy tailed and heavily skewed. This, in turn, leads to the variance of estimators being poorly estimated, and confidence intervals having actual coverage quite different from (typically lower than) the nominal one. We illustrate these general ideas by estimating the mean occupancy and tail probabilities in M/G/1 queues, comparing confidence intervals computed from batch means to various intervals computed from regenerative cycles. In addition, we provide theoretical results on skewness to support the empirical findings.

  • 7.
    Ejaz Ahmed, S.
    et al.
    Brock University, Canada.
    Fallahpour, Saber
    University of Windsor, Canada.
    von Rosen, Dietrich
    Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, Faculty of Science & Engineering. Swedish University of Agriculture Science, Sweden.
    von Rosen, Tatjana
    Stockholm University, Sweden.
    Estimation of Several Intraclass Correlation Coefficients2015In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 44, no 9, p. 2315-2328Article in journal (Refereed)
    Abstract [en]

    An intraclass correlation coefficient observed in several populations is estimated. The basis is a variance-stabilizing transformation. It is shown that the intraclass correlation coefficient from any elliptical distribution should be transformed in the same way. Four estimators are compared. An estimator where the components in a vector consisting of the transformed intraclass correlation coefficients are estimated separately, an estimator based on a weighted average of these components, a pretest estimator where the equality of the components is tested and then the outcome of the test is used in the estimation procedure, and a James-Stein estimator which shrinks toward the mean.

  • 8. Ekblom, Håkan
    Generation of test problems for Lp - and Huber regression1990In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 19, no 2, p. 481-489Article in journal (Refereed)
    Abstract [en]

    Numerical methods for obtaining (X|y), related to the linear model y = X + e, are presented. The user is allowed to specify the Lp or Huber solution vector B* and is also free to choose the conditioning and the structure of X.

  • 9.
    Holgersson, Thomas
    Högskolan i Jönköping.
    A Modified Skewness Measure for Testing Asymmetry2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 2, p. 335-346Article in journal (Refereed)
    Abstract [en]

    Statistical practitioners frequently wish to know whether a variable is symmetrically distributed. There are a number of different tests available but the most commonly used one is perhaps that based on the standardized third central moment, as defined by Pearson and Fisher in the early 1900's. While this traditional skewness measure uniquely determines the symmetry of a variable within the Pearson family, it does not uniquely determine symmetry for a general distribution. In this article, we propose a modified version of the classical skewness test which is easy to conduct and consistent against a wide family of asymmetric distributions.

  • 10.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    A Modified Skewness Measure for Testing Asymmetry2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 2, p. 335-346Article in journal (Refereed)
  • 11.
    Holgersson, Thomas
    et al.
    Högskolan i Jönköping.
    Lindström, Fredrik
    Göteborgs universitet.
    A Comparison of Conditioned Versus Unconditioned Forecasts of the VAR(1) Process2005In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 34, no 2, p. 415-427Article in journal (Refereed)
    Abstract [en]

    The properties of a forecast usually depend upon whether or not the forecast is conditioned on the final period observation. In the case of unconditioned forecasts, it is well known that the point predictions are unbiased. If, on the other hand, the forecast is conditional, then the forecast may be biased. Existing analytical results in literature are insufficient for describing the properties of the conditioned forecast properly, particularly in multivariate models. This article examines some finite sample properties of conditioned forecasts of the VAR(1) process by means of Monte Carlo experiments. We use a number of parameter settings for the VAR(1) process to demonstrate that the forecast bias of the conditioned forecast may be considerable. Hence, unless the analyst has a clear idea of whether the conditioned or unconditioned forecast is relevant for the time series being analyzed, statistical inferences may be seriously erratic.

  • 12.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Lindström, Fredrik
    Göteborg University, Dept. of statistics.
    A Comparison of Conditioned Versus Unconditioned Forecasts of the VAR(1) Process2005In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 34, p. 415-427Article in journal (Refereed)
  • 13.
    Holgersson, Thomas
    et al.
    Jönköping University.
    Mansoor, Rashid
    Jönköping University.
    Assessing Normality of High-Dimensional Data2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 2, p. 360-369Article in journal (Refereed)
    Abstract [en]

    The assumption of normality is crucial in many multivariate inference methods and may be even more important when the dimension of data is proportional to the sample size. It is therefore necessary that tests for multivariate non normality remain well behaved in such settings. In this article, we examine the properties of three common moment-based tests for non normality under increasing dimension asymptotics (IDA). It is demonstrated through Monte Carlo simulations that one of the tests is inconsistent under IDA and that one of them stands out as uniformly superior to the other two.

  • 14.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Mansoor, Rashid
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Assessing Normality of High-Dimensional Data2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 2, p. 360-369Article in journal (Refereed)
    Abstract [en]

    The assumption of normality is crucial in many multivariate inference methods and may be even more important when the dimension of data is proportional to the sample size. It is therefore necessary that tests for multivariate non normality remain well behaved in such settings. In this article, we examine the properties of three common moment-based tests for non normality under increasing dimension asymptotics (IDA). It is demonstrated through Monte Carlo simulations that one of the tests is inconsistent under IDA and that one of them stands out as uniformly superior to the other two.

  • 15.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Linnaeus University, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Göteborg University, Sweden .
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Testing for panel unit roots under general cross-sectional dependence2016In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 45, no 5, p. 1785-1801Article in journal (Refereed)
    Abstract [en]

    In this paper we generalize four tests of multivariate linear hypothesis to panel data unit root testing. The test statistics are invariant to certain linear transformations of data and therefore simulated critical values may conveniently be used. It is demonstrated that all four tests remains well behaved in cases of where there are heterogeneous alternatives and cross-correlations between marginal variables. A Monte Carlo simulation is included to compare and contrast the tests with two well-established ones.

  • 16.
    Holgersson, Thomas
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    Månsson, Kristofer
    University of Gothenburg.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    Testing for Panel Unit Roots under General Cross-Sectional Dependence2016In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 45, no 5, p. 1785-1801Article in journal (Refereed)
    Abstract [en]

    In this paper we generalize four tests of multivariate linear hypothesis to panel data unit root testing. The test statistics are invariant to certain linear transformations of data and therefore simulated critical values may conveniently be used. It is demonstrated that all four tests remains well behaved in cases of where there are heterogeneous alternatives and cross-correlations between marginal variables. A Monte Carlo simulation is included to compare and contrast the tests with two well-established ones.

  • 17.
    Holgersson, Thomas
    et al.
    Göteborg University.
    Shukur, Ghazi
    Göteborg University.
    Some Aspects of Non-Normality Tests in Systems of Regressions Equations2001In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 30, no 2, p. 291-310Article in journal (Refereed)
    Abstract [en]

    In this paper, a short background of the Jarque and McKenzie (JM) test for non-normality is given, and the small sample properties of the test is examined in view of robustness, size and power. The investigation has been performed using Monte Carlo simulations where factors like, e.g., the number of equations, nominal sizes, degrees of freedom, have been varied.

    Generally, the JM test has shown to have good power properties. The estimated size due to the asymptotic distribution is not very encouraging though. The slow rate of convergence to its asymptotic distribution suggests that empirical critical values should be used in small samples.

    In addition, the experiment shows that the properties of the JM test may be disastrous when the disturbances are autocorrelated. Moreover, the simulations show that the distribution of the regressors may also have a substantial impact on the test, and that homogenised OLS residuals should be used when testing for non-normality in small samples.

  • 18.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Some Aspects of Non-Normality Tests in Systems of Regressions Equations2001In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 30, no 2, p. 291-310Article in journal (Refereed)
    Abstract [en]

    In this paper, a short background of the Jarque and McKenzie (JM) test for non-normality is given, and the small sample properties of the test is examined in view of robustness, size and power. The investigation has been performed using Monte Carlo simulations where factors like, e.g., the number of equations, nominal sizes, degrees of freedom, have been varied.

    Generally, the JM test has shown to have good power properties. The estimated size due to the asymptotic distribution is not very encouraging though. The slow rate of convergence to its asymptotic distribution suggests that empirical critical values should be used in small samples.

    In addition, the experiment shows that the properties of the JM test may be disastrous when the disturbances are autocorrelated. Moreover, the simulations show that the distribution of the regressors may also have a substantial impact on the test, and that homogenised OLS residuals should be used when testing for non-normality in small samples.

  • 19. Hussain, S.
    et al.
    Mohamed, M. A.
    Holder, R.
    Almasri, Abdullah
    Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Economics and Statistics.
    Shukur, G.
    Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modeling Using Two New Strategies2008In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 37, no 10, p. 1966-1980Article in journal (Refereed)
  • 20. Hussain, Shakir
    et al.
    A. Mohamed, Mohamed
    Holder, Roger
    Almasri, Abdullah
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modeling Using Two New Strategies2008In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 37, no 10, p. 1966-1980Article in journal (Refereed)
  • 21. Hussain, Shakir
    et al.
    Mohamed, A
    Holder, Roger
    Almasri, Abdullah
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modelling Using Two New Strategies2008In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 37, no 10, p. 1966-1980Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a general framework for performance evaluation of organizations and individuals over time using routinely collected performance variables or indicators. Such variables or indicators are often correlated over time, with missing observations, and often come from heavy-tailed distributions shaped by outliers. Two new double robust and model-free strategies are used for evaluation (ranking) of sampling units. Strategy 1 can handle missing data using residual maximum likelihood (RML) at stage two, while strategy two handles missing data at stage one. Strategy 2 has the advantage that overcomes the problem of multicollinearity. Strategy one requires independent indicators for the construction of the distances, where strategy two does not. Two different domain examples are used to illustrate the application of the two strategies. Example one considers performance monitoring of gynecologists and example two considers the performance of industrial firms.

  • 22.
    Häggström, Jenny
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Estimating prediction error: cross-validation vs. accumulated prediction error2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 5, p. 880-898Article in journal (Refereed)
    Abstract [en]

    We study the validation of prediction rules such as regression models and classification algorithms through two out-of-sample strategies, cross-validation and accumulated prediction error. We use the framework of Efron (1983) where measures of prediction errors are defined as sample averages of expected errors and show through exact finite sample calculations that cross-validation and accumulated prediction error yield different smoothing parameter choices in nonparametric regression. The difference in choice does not vanish as sample size increases.

  • 23.
    Javed, Farrukh
    et al.
    Lund University.
    Mantalos, Panagiotis
    Örebro University.
    GARCH-Type Models and Performance of Information Criteria2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 8, p. 1917-1933Article in journal (Refereed)
    Abstract [en]

    This article discusses the ability of information criteria toward the correct selection of different especially higher-order generalized autoregressive conditional heteroscedasticity (GARCH) processes, based on their probability of correct selection as a measure of performance. Each of the considered GARCH processes is further simulated at different parameter combinations to study the possible effect of different volatility structures on these information criteria. We notice an impact from the volatility structure of time series on the performance of these criteria. Moreover, the influence of sample size, having an impact on the performance of these criteria toward correct selection, is observed.

  • 24.
    Javed, Farrukh
    et al.
    Dept of Statistics, Lund University, Lund, Sweden.
    Mantalos, Panagiotis
    Örebro University, Örebro University School of Business.
    GARCH-Type Models and Performance of Information Criteria2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 8, p. 1917-1933Article in journal (Refereed)
    Abstract [en]

    This article discusses the ability of information criteria toward the correct selection of different especially higher-order generalized autoregressive conditional heteroscedasticity (GARCH) processes, based on their probability of correct selection as a measure of performance. Each of the considered GARCH processes is further simulated at different parameter combinations to study the possible effect of different volatility structures on these information criteria. We notice an impact from the volatility structure of time series on the performance of these criteria. Moreover, the influence of sample size, having an impact on the performance of these criteria toward correct selection, is observed.

  • 25.
    Järpe, Eric
    et al.
    Department of Statistics, Gothenburg University, Gothenburg, Sweden.
    Wessman, Peter
    Department of Statistics, Gothenburg University, Gothenburg, Sweden.
    Some Power Aspects of Methods for Detecting Different Shifts in the Mean2000In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 29, no 2, p. 633-646Article in journal (Refereed)
    Abstract [en]

    We study, by means of simulations, the performance of the Shewhart method, the Cusum method, the Shiryaev-Roberts method and the likelihood ratio method in the case when the true shift differs from the shift for which the methods are optimal. The methods are compared for a fixed expected time until false alarm. The comparisons are made with respect to some measures associated with power such as probability of alarm when the change occurs immediately, expected delay of true alarm and predictive value of an alarm. Copyright © 2000 by Marcel Dekker, Inc.

  • 26.
    Karlsson, Andreas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centre for Clinical Research, County of Västmanland.
    Nonlinear quantile regression estimation of longitudinal data2008In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 37, no 1, p. 114-131Article in journal (Refereed)
    Abstract [en]

    This article examines a weighted version of the quantile regression estimator as defined by Koenker and Bassett (1978), adjusted to the case of nonlinear longitudinal data. Using a four-parameter logistic growth function and error terms following an AR(1) model, different weights are used and compared in a simulation study. The findings indicate that the nonlinear quantile regression estimator is performing well, especially for the median regression case, that the differences between the weights are small, and that the estimator performs better when the correlation in the AR(1) model increases. A comparison is also made with the corresponding mean regression estimator, which is found to be less robust. Finally, the estimator is applied to a data set with growth patterns of two genotypes of soybean, which gives some insights into how the quantile regressions provide a more complete picture of the data than the mean regression.

  • 27.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Finite sample properties of the QME2004In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 33, no 3, p. 567-583Article in journal (Refereed)
    Abstract [en]

    Bias and MSE of the QME are studied by means of simulation. A bootstrap estimator of the QME covariance matrix is also included in the study. The simulation is based on travel distances reported in the Swedish Travel Habit Survey. The results are in accordance with the asymptotic properties of the QME. For example, the QME is better than other suggested estimators under asymmetric distributions of the error term. The results also suggest that the bootstrap technique is potentially useful for estimation of the QME covariance matrix.

  • 28.
    Kibria, B. M. Golam
    et al.
    Florida International University.
    Månsson, Kristofer
    Internationella Handelshögskolan i Jönköping.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    A simulation study of some biasing parameters for the ridge type estimation of Poisson regression2015In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 44, no 4, p. 943-957Article in journal (Refereed)
    Abstract [en]

    This paper proposes several estimators for estimating the ridge parameter k based for Poisson ridge regression (RR) model. These estimators have been evaluated by means of Monte Carlo simulations. As performance criteria, we have calculated the mean squared error (MSE), the mean value and the standard deviation of k. The first criterion is commonly used, while the other two have never been used when analyzing Poisson RR. However, these performance criterion are very informative because, if several estimators have an equal estimated MSE then those with low average value and standard deviation of k should be preferred. Based on the simulated results we may recommend some biasing parameters which may be useful for the practitioners in the field of health, social and physical sciences.

  • 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, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A simulation study of some biasing parameters for the ridge type estimation of Poisson regression2015In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 44, no 4, p. 943-957Article in journal (Refereed)
    Abstract [en]

    This paper proposes several estimators for estimating the ridge parameter k based for Poisson ridge regression (RR) model. These estimators have been evaluated by means of Monte Carlo simulations. As performance criteria, we have calculated the mean squared error (MSE), the mean value and the standard deviation of k. The first criterion is commonly used, while the other two have never been used when analyzing Poisson RR. However, these performance criterion are very informative because, if several estimators have an equal estimated MSE then those with low average value and standard deviation of k should be preferred. Based on the simulated results we may recommend some biasing parameters which may be useful for the practitioners in the field of health, social and physical sciences.

  • 30.
    Li, Yushu
    et al.
    Department of Economic and Statistics, Center for Labor Market Policy Research (CAFO), Linaeus University, Växjö, Sweden.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Testing for Unit Root Against LSTAR Models: Wavelet Improvement under GARCH Distortion2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 2, p. 277-286Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a nonlinear Dickey-Fuller F test for unit root against first-order Logistic Smooth Transition Autoregressive (LSTAR) (1) model with time as the transition variable. The nonlinear Dickey-Fuller F test statistic is established under the null hypothesis of random walk without drift and the alternative model is a nonlinear LSTAR (1) model. The asymptotic distribution of the test is analytically derived while the small sample distributions are investigated by Monte Carlo experiment. The size and power properties of the test were investigated using Monte Carlo experiment. The results showed that there is a serious size distortion for the test when GARCH errors appear in the Data Generating Process (DGP), which led to an over-rejection of the unit root null hypothesis. To solve this problem, we use the Wavelet technique to count off the GARCH distortion and improve the size property of the test under GARCH error. We also discuss the asymptotic distributions of the test statistics in GARCH and wavelet environments.

  • 31.
    Li, Yushu
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Testing for Unit Root Against LSTAR Models: Wavelet Improvement under GARCH Distortion2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 2, p. 277-286Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a nonlinear Dickey-Fuller F test for unit root against first-order Logistic Smooth Transition Autoregressive (LSTAR) (1) model with time as the transition variable. The nonlinear Dickey-Fuller F test statistic is established under the null hypothesis of random walk without drift and the alternative model is a nonlinear LSTAR (1) model. The asymptotic distribution of the test is analytically derived while the small sample distributions are investigated by Monte Carlo experiment. The size and power properties of the test were investigated using Monte Carlo experiment. The results showed that there is a serious size distortion for the test when GARCH errors appear in the Data Generating Process (DGP), which led to an over-rejection of the unit root null hypothesis. To solve this problem, we use the Wavelet technique to count off the GARCH distortion and improve the size property of the test under GARCH error. We also discuss the asymptotic distributions of the test statistics in GARCH and wavelet environments.

  • 32.
    Locking, Håkan
    et al.
    Department of Economics and Statistics, Centre for Labour Market and Discrimination Studies, Linnaeus University, Växjö, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Performance of Some Ridge Parameters for Probit Regression: With Application to Swedish Job Search Data2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 3, p. 698-710Article in journal (Refereed)
    Abstract [en]

    In ridge regression, the estimation of the ridge parameter is an important issue. This article generalizes some methods for estimating the ridge parameter for probit ridge regression (PRR) model based on the work of Kibria et al. (2011). The performance of these new estimators is judged by calculating the mean squared error (MSE) using Monte Carlo simulations. In the design of the experiment, we chose to vary the sample size and the number of regressors. Furthermore, we generate explanatory variables that are linear combinations of other regressors, which is a common situation in economics. In an empirical application regarding Swedish job search data, we also illustrate the benefits of the new method.

  • 33.
    Locking, Håkan
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Månsson, Kristofer
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Performance of Some Ridge Parameters for Probit Regression: With Application to Swedish Job Search Data2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 3, p. 698-710Article in journal (Refereed)
    Abstract [en]

    In ridge regression the estimation of the ridge parameter is an important issue. This paper generalizes some methods for estimating the ridge parameter for probit ridge regression (PRR) model based on the work of Kibria et al. (2011). The performance of these new estimators are judged by calculating the mean square error (MSE) usingMonte Carlo simulations.  In the design of the experiment we chose to vary the sample size and the number of regressors. Furthermore, we generate explanatory variables that are linear combinations of other regressors, which is a common situation in economics. In an empirical application regarding Swedish job search data we also illustrate the benefits of the new method.

  • 34.
    Lyhagen, Johan
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Information Science.
    A method to generate multivariate data with the desired moments2008In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 37, no 10, p. 2063-2075Article in journal (Refereed)
    Abstract [en]

    We show how it is possible to generate multivariate data which has moments arbitrary close to the desired ones. They are generated as linear combinations of variables with known theoretical moments. It is shown how to derive the weights of the linear combinations in both the univariate and the multivariate setting. The use in bootstrapping is discussed and the method is exemplified with a Monte Carlo simulation where the importance of the ability of generating data with control of higher moments is shown.

  • 35.
    Mantalos, Panagiotis
    et al.
    University of Lund.
    Mattheou, K.
    University of Cyprus, Cyprus.
    Karagrigoriou, A.
    University of Cyprus, Cyprus.
    An Improved Divergence Information Criterion for the Determination of the Order of an AR Process2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 5, p. 865-879Article in journal (Refereed)
    Abstract [en]

    In this article we propose a modification of the recently introduced divergence information criterion (DIC, Mattheou et al., 2009) for the determination of the order of an autoregressive process and show that it is an asymptotically unbiased estimator of the expected overall discrepancy, a nonnegative quantity that measures the distance between the true unknown model and a fitted approximating model. Further, we use Monte Carlo methods and various data generating processes for small, medium, and large sample sizes in order to explore the capabilities of the new criterion in selecting the optimal order in autoregressive processes and in general in a time series context. The new criterion shows remarkably good results by choosing the correct model more frequently than traditional information criteria.

  • 36.
    Mantalos, Panagiotis
    et al.
    Department of Statistics , University of Lund , Lund, Sweden.
    Mattheou, K.
    Department of Mathematics and Statistics , University of Cyprus , Nicosia, Cyprus.
    Karagrigoriou, A.
    Department of Mathematics and Statistics , University of Cyprus , Nicosia, Cyprus.
    An Improved Divergence Information Criterion for the Determination of the Order of an AR Process2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 5, p. 865-879Article in journal (Refereed)
    Abstract [en]

    In this article we propose a modification of the recently introduced divergence information criterion (DIC, Mattheou et al., 2009) for the determination of the order of an autoregressive process and show that it is an asymptotically unbiased estimator of the expected overall discrepancy, a nonnegative quantity that measures the distance between the true unknown model and a fitted approximating model. Further, we use Monte Carlo methods and various data generating processes for small, medium, and large sample sizes in order to explore the capabilities of the new criterion in selecting the optimal order in autoregressive processes and in general in a time series context. The new criterion shows remarkably good results by choosing the correct model more frequently than traditional information criteria.

  • 37.
    Mishchenko, Kateryna
    et al.
    Mälardalen University, School of Education, Culture and Communication.
    Neytcheva, Maya
    Uppsala University.
    New Algorithms for Evaluating the Log-likelihood Function Derivatives in the AI-REML Method2009In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 38, no 6, p. 1348-1364Article in journal (Refereed)
    Abstract [en]

     In this study, we propose several improvements of the Average Information Restricted Maximum Likelihood algorithms for estimating the variance components for genetic mapping of quantitative traits. The improved methods are applicable when two variance components are to be estimated. The improvements are related to the algebraic part of the methods and utilize the properties of the underlying matrix structures. In contrast to previously developed algorithms, the explicit computation of a matrix inverse is replaced by the solution of a linear system of equations with multiple right-hand sides, based on a particular matrix decomposition. The computational costs of the proposed algorithms are analyzed and compared.

  • 38. Mishchenko, Kateryna
    et al.
    Neytcheva, Maya
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    New algorithms for evaluating the log-likelihood function derivatives in the AI-REML method2009In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 38, p. 1348-1364Article in journal (Refereed)
  • 39. Månsson, Kristofer
    et al.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics.
    Granger Causality Test in the Presence of Spillover Effects2009In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 38, no 10, p. 2039-2059Article in journal (Refereed)
    Abstract [en]

    In this article, we investigate the effect of spillover (i.e., causality in variance) on the reliability of Granger causality test based on ordinary least square estimates. We studied eight different versions of the test both, with and without Whites heteroskedasticity consistent covariance matrix (HCCME). The properties of the tests are investigated by means of a Monte Carlo experiment where 21 different data generating processes (DGP) are used and a number of factors that might affect the test are varied. The result shows that the best choice to test for Granger causality under the presence of spillover is the Lagrange Multiplier test with HCCME.

  • 40.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Granger Causality Test in the Presence of Spillover Effects2009In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 38, no 10, p. 2039-2059Article in journal (Refereed)
    Abstract [en]

    In this article, we investigate the effect of spillover (i.e., causality in variance) on the reliability of Granger causality test based on ordinary least square estimates. We studied eight different versions of the test both, with and without Whites heteroskedasticity consistent covariance matrix (HCCME). The properties of the tests are investigated by means of a Monte Carlo experiment where 21 different data generating processes (DGP) are used and a number of factors that might affect the test are varied. The result shows that the best choice to test for Granger causality under the presence of spillover is the Lagrange Multiplier test with HCCME.

  • 41.
    Pingel, Ronnie
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    Waernbaum, Ingeborg
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik; Institutet för arbetsmarknads- och utbildningspolitisk utvärdering, IFAU.
    Correlation and Efficiency of Propensity Score-based Estimators for Average Causal Effects2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 5, p. 3458-3478Article in journal (Refereed)
    Abstract [en]

    Propensity score based-estimators are commonly used to estimate causal effects in evaluationresearch. To reduce bias in observational studies researchers might be tempted to include many, perhaps correlated, covariates when estimating the propensity score model. Taking into account that the propensity score is estimated, this study investigates how the efficiency of matching, inverse probability weighting and doubly robust estimators change under the case of correlated covariates. Propositions regarding the large sample variances under certain assumptions on the data generating process are given. The propositions are supplemented by several numerical large sample and finite sample results from a wide range of models. The results show that the covariate correlations may increase or decrease the variances of the estimators. There are several factors that influence how correlation affects the variance of the estimators, including the choice of estimator, the strength of the confounding towards outcome and treatment, and whether a constant or non-constant causal effect is present.

  • 42. Pingel, Ronnie
    et al.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Institutet för arbetsmarknads- och utbildningspolitisk utvärdering, IFAU.
    Correlation and Efficiency of Propensity Score-based Estimators for Average Causal Effects2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 5, p. 3458-3478Article in journal (Refereed)
    Abstract [en]

    Propensity score based-estimators are commonly used to estimate causal effects in evaluationresearch. To reduce bias in observational studies researchers might be tempted to include many, perhaps correlated, covariates when estimating the propensity score model. Taking into account that the propensity score is estimated, this study investigates how the efficiency of matching, inverse probability weighting and doubly robust estimators change under the case of correlated covariates. Propositions regarding the large sample variances under certain assumptions on the data generating process are given. The propositions are supplemented by several numerical large sample and finite sample results from a wide range of models. The results show that the covariate correlations may increase or decrease the variances of the estimators. There are several factors that influence how correlation affects the variance of the estimators, including the choice of estimator, the strength of the confounding towards outcome and treatment, and whether a constant or non-constant causal effect is present.

  • 43.
    Riazoshams, Hossein
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics. Islamic Azad University, Iran.
    Midi, Habshah Bt.
    The Performance of a Robust Multistage Estimator in Nonlinear Regression with Heteroscedastic Errors2016In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 45, no 9, p. 3394-3415Article, review/survey (Refereed)
    Abstract [en]

    In this article, a robust multistage parameter estimator is proposed for nonlinear regression with heteroscedastic variance, where the residual variances are considered as a general parametric function of predictors. The motivation is based on considering the chi-square distribution for the calculated sample variance of the data. It is shown that outliers that are influential in nonlinear regression parameter estimates are not necessarily influential in calculating the sample variance. This matter  persuades us, not only to robustify the estimate of the parameters of the models for both the regression function and the variance, but also to replace the sample variance of the data by a robust scale estimate.

  • 44.
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    The Robustness of the Systemwise Breauch-Godfrey Autocorrelation Test for Non-normal Error Terms2000In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 29, no 2, p. 419-448Article in journal (Refereed)
  • 45.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The Robustness of the Systemwise Breusch-Godfrey Autocorrelation Test for Non-normal Distributed Error Terms2000In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 29, no 2, p. 419-448Article in journal (Refereed)
  • 46.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Doszyń, Mariusz
    Szczecin University, Econometrics and Statistics Institute.
    Dmytrów, Krzysztof
    Szczecin University, Econometrics and Statistics Institute.
    Comparison of the effectiveness of forecasts obtained by means of selected probability functions with respect to forecast error distributions2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 5, p. 3667-3679Article in journal (Refereed)
    Abstract [en]

    The Forecasting of sales in a company is one of the crucial challenges that must be faced. Nowadays, there is a large spectrum of methods that enable making reliable forecasts. However, sometimes the nature of time series excludes many well-known and widely used forecasting methods (e.g. econometric models). Therefore, the authors decided to forecast on the basis of a seasonally adjusted median of selected probability distributions. The obtained forecasts were verified by means of distributions of the Theil U2 coefficient and unbiasedness coefficient.

  • 47.
    Shukur, Ghazi
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Doszyń, Mariusz
    Szczecin University, Poland.
    Dmytrów, Krzysztof
    Szczecin University, Poland.
    Comparison of the effectiveness of forecasts obtained by means of selected probability functions with respect to forecast error distributions2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 5, p. 3667-3679Article in journal (Refereed)
    Abstract [en]

    The Forecasting of sales in a company is one of the crucial challenges that must be faced. Nowadays, there is a large spectrum of methods that enable making reliable forecasts. However, sometimes the nature of time series excludes many well-known and widely used forecasting methods (e.g. econometric models). Therefore, the authors decided to forecast on the basis of a seasonally adjusted median of selected probability distributions. The obtained forecasts were verified by means of distributions of the Theil U2 coefficient and unbiasedness coefficient.

  • 48.
    Shukur, Ghazi
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Holgersson, Thomas
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Some Aspects of Non-Normality Tests in Systems of Regression Equations2001In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 30, no 2, p. 291-310Article in journal (Refereed)
  • 49.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Kibria, B. M. Golam
    A Simulation Study of Some Ridge Regression Estimators under Different Distributional Assumptions2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 8, p. 1639-1670Article in journal (Refereed)
  • 50.
    Shukur, Ghazi
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Månsson, Kristofer
    Kibria, B. M. Golam
    A Simulation Study of Some Ridge Regression Estimators under Different Distributional Assumptions 2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 8, p. 1639-1670Article in journal (Refereed)
    Abstract [en]

    Based on the work of Khalaf and Shukur (2005), Alkhamisi et al. (2006), and Muniz et al. (2010), this article considers several estimators for estimating the ridge parameter k. This article differs from aforementioned articles in three ways: (1) Data are generated from Normal, Student's t, and F distributions with appropriate degrees of freedom; (2) The number of regressors considered are from 4-12 instead of 2-4, which are the usual practice; (3) Both mean square error (MSE) and prediction sum of square (PRESS) are considered as the performance criterion. A simulation study has been conducted to compare the performance of the estimators. Based on the simulation study we found that, increasing the correlation between the independent variables has negative effect on the MSE and PRESS. However, increasing the number of regressors has positive effect on MSE and PRESS. When the sample size increases the MSE decreases even when the correlation between the independent variables is large. It is interesting to note that the dominance pictures of the estimators are remained the same under both the MSE and PRESS criterion. However, the performance of the estimators depends on the choice of the assumption of the error distribution of the regression model.

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