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  • 1.
    Aires, Nibia
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    A guide to the Fortran programs to calculate inclusion probabilities for conditional Poisson sampling and Pareto pi ps sampling designs2004In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 19, no 3, p. 337-345Article in journal (Refereed)
    Abstract [en]

    Conditional Poisson Sampling and Pareto pips Sampling designs are sampling methods with fixed sample size and with inclusion probabilities proportional to given size measures.. Algorithms were introduced to calculate first and second exact inclusion probabilities for both schemes. Methods were also provided to adjust the parameters to get predetermined inclusion probabilities. In this paper, the Fortran procedures are introduced and documented. Moreover, guidelines are provided for their use as well as examples and the programs codes commented.

  • 2.
    Amiri, Saeid
    et al.
    Univ Wisconsin, Dept Nat & Appl Sci, Green Bay, WI 54302 USA..
    Modarres, Reza
    George Washington Univ, Dept Stat, Washington, DC 20052 USA..
    Zwanzig, Silvelyn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.
    Tests of perfect judgment ranking using pseudo-samples2017In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 32, no 4, p. 1309-1322Article in journal (Refereed)
    Abstract [en]

    Ranked set sampling (RSS) is a sampling approach that can produce improved statistical inference when the ranking process is perfect. While some inferential RSS methods are robust to imperfect rankings, other methods may fail entirely or provide less efficiency. We develop a nonparametric procedure to assess whether the rankings of a given RSS are perfect. We generate pseudo-samples with a known ranking and use them to compare with the ranking of the given RSS sample. This is a general approach that can accommodate any type of raking, including perfect ranking. To generate pseudo-samples, we consider the given sample as the population and generate a perfect RSS. The test statistics can easily be implemented for balanced and unbalanced RSS. The proposed tests are compared using Monte Carlo simulation under different distributions and applied to a real data set.

  • 3.
    Andersson, Michael K.
    et al.
    National Institute of Economic Research, Stockholm, Sweden.
    Karlsson, Sune
    Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Bootstrapping Error Component Models2001In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 16, no 2, p. 221-231Article in journal (Refereed)
    Abstract [en]

    This paper proposes several resampling algorithms suitable for error component models and evaluates them in the context of bootstrap testing. In short, all the algorithms work well and lead to tests with correct or close to correct size. There is thus little or no reason not to use the bootstrap with error component models.

  • 4.
    Brännäs, Kurt
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    De Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Generalized method of moment and indirect estimation of the ARasMA model1998In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 13, no 4, p. 485-494Article in journal (Refereed)
    Abstract [en]

    Estimation in nonlinear time series models has mainly been performed by least squares or maximum likelihood (ML) methods. The paper suggests and studies the performance of generalized method of moments (GMM) and indirect estimators for the autoregressive asymmetric moving average model. Both approaches are easy to implement and perform well numerically. In a Monte Carlo study it is found that the MSE properties of GMM are close to those of ML. The indirect estimator performs poorly in this respect. On the other hand, the three estimation techniques lead to fairly similar power functions for a linearity test.

  • 5.
    Edlund, Ove
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Ekblom, Håkan
    Madsen, Kaj
    Institute of Mathematical Modelling, Technical University of Denmark.
    Algorithms for non-linear M-estimation1997In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 12, no 3, p. 373-383Article in journal (Refereed)
  • 6.
    Flygare, Ann-Marie
    et al.
    Umeå University, Umeå, Sweden.
    Barrlund, Anders
    Umeå University, Umeå, Sweden.
    Efficient implementation of some contextual classification methods1995In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 10, no 4, p. 327-338Article in journal (Refereed)
  • 7.
    Gredenhoff, Mikael
    et al.
    Dept. of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Karlsson, Sune
    Dept. of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Lag-length selection in VAR-models using equal and unequal lag-length procedures1999In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 14, no 2, p. 171-187Article in journal (Refereed)
    Abstract [en]

    It is well known that inference in vector autoregressive models depends crucially on the choice of lag-length. Various lag-length selection procedures have been suggested and evaluated in the literature. In these evaluations the possibility that the true model may have unequal lag-length has, however, received little attention. In this paper we investigate how sensitive lag-length estimation procedures, based on assumptions of equal or unequal lag-lengths, are to the true model structure. The procedures used in the paper are based on information criteria and we give results for AIC, HQ and BIG. In the Monte Carlo study we generate data from a variety of VAR models with properties similar to macro-economic time-series. We find that the commonly used procedure based on equal lag-length together with AIC and HQ performs well in most cases. The procedure (due to Hsiao) allowing for unequal lag-lengths produce reasonable results when the true model has unequal lag-length. The Hsiao procedure tend to do better than equal lag-length procedures in models with a more complicated lag structure.

  • 8.
    Holgersson, Thomas
    Högskolan i Jönköping.
    A Graphical Technique for Assessing Multivariate Non-normality2006In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 21, no 1, p. 141-149Article in journal (Refereed)
    Abstract [en]

    In this paper we suggest a simple graphical device for assessing multivariate normality. The method is based on the characteristic that linear combinations of the sample mean and sample covariance matrix are independent if and only if the random variable is normally distributed. We demonstrate the usage of the suggested method and compare it to the classical Q-Q plot by using some multivariate data sets.

  • 9.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    A Graphical Technique for Assessing Multivariate Non-normality2006In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 21, no 1, p. 141-149Article in journal (Refereed)
  • 10.
    Häggström, Jenny
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Targeted smoothing parameter selection for estimating average causal effects2014In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 29, no 6, p. 1727-1748Article in journal (Refereed)
    Abstract [en]

    The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as kernel, splines or local polynomial regression. Such regression methods are tuned via smoothing parameters which regulates the amount of degrees of freedom used in the fit. In this paper we propose data-driven methods for selecting smoothing parameters when the targeted parameter is an average causal effect. For this purpose, we propose to estimate the exact expression of the mean squared error of the estimators. Asymptotic approximations indicate that the smoothing parameters minimizing this mean squared error converges to zero faster than the optimal smoothing parameter for the estimation of the regression functions. In a simulation study we show that the proposed data-driven methods for selecting the smoothing parameters yield lower empirical mean squared error than other methods available such as, e.g., cross-validation.

  • 11.
    Jentsch, Carlstein
    et al.
    University of Mannheim, Germany.
    Kreiss, Jens P.
    Technische Universität Braunschweig, Germany.
    Mantalos, Panagiotis
    Örebro University.
    Paparoditis, Efstathios
    University of Cyprus, Cyprus .
    Hybrid bootstrap aided unit root testing2012In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 27, no 4, p. 779-797Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a hybrid bootstrap procedure for augmented Dickey-Fuller (ADF) tests for the presence of a unit root. This hybrid proposal combines a time domain parametric autoregressive fit to the data and a nonparametric correction applied in the frequency domain to capture features that are possibly not represented by the parametric model. It is known that considerable size and power problems can occur in small samples for unit root testing in the presence of an MA parameter using critical values of the asymptotic Dickey-Fuller distribution. The benefit of the sieve bootstrap in this situation has been investigated by Chang and Park (J Time Ser Anal 24:379–400, 2003). They showed asymptotic validity as well as substantial improvements for small sample sizes, but the actual sizes of their bootstrap tests were still quite far away from the nominal size. The finite sample performances of our procedure are extensively investigated through Monte Carlo simulations and compared to the sieve bootstrap approach. Regarding the size of the tests, our results show that the hybrid bootstrap remarkably outperforms the sieve bootstrap.

  • 12.
    Jentsch, Carlstein
    et al.
    Department of Economics, University of Mannheim, Mannheim, Germany .
    Kreiss, Jens P.
    Institut für Mathematische Stochastik, Technische Universität Braunschweig, Braunschweig, Germany .
    Mantalos, Panagiotis
    Örebro University, Örebro University School of Business.
    Paparoditis, Efstathios
    Department of Mathematics and Statistics, University of Cyprus, Nicosia, Cyprus .
    Hybrid bootstrap aided unit root testing2012In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 27, no 4, p. 779-797Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a hybrid bootstrap procedure for augmented Dickey-Fuller (ADF) tests for the presence of a unit root. This hybrid proposal combines a time domain parametric autoregressive fit to the data and a nonparametric correction applied in the frequency domain to capture features that are possibly not represented by the parametric model. It is known that considerable size and power problems can occur in small samples for unit root testing in the presence of an MA parameter using critical values of the asymptotic Dickey-Fuller distribution. The benefit of the sieve bootstrap in this situation has been investigated by Chang and Park (J Time Ser Anal 24:379–400, 2003). They showed asymptotic validity as well as substantial improvements for small sample sizes, but the actual sizes of their bootstrap tests were still quite far away from the nominal size. The finite sample performances of our procedure are extensively investigated through Monte Carlo simulations and compared to the sieve bootstrap approach. Regarding the size of the tests, our results show that the hybrid bootstrap remarkably outperforms the sieve bootstrap.

  • 13. Johansson, Per
    et al.
    Bergkvist, E.
    Weighted derivative estimation of quantal response model: simulations and applications to choice of truck freight carrier2000In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 15, p. 485-511Article in journal (Refereed)
  • 14.
    Luukko, P. J. J.
    et al.
    Univ Jyvaskyla, Finland.
    Helske, Jouni
    Univ Jyvaskyla, Finland.
    Rasanen, E.
    Tampere Univ Technol, Finland.
    Introducing libeemd: a program package for performing the ensemble empirical mode decomposition2016In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 31, no 2, p. 545-557Article in journal (Refereed)
    Abstract [en]

    The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode decomposition (EMD). All these methods decompose possibly nonlinear and/or nonstationary time series data into a finite amount of components separated by instantaneous frequencies. This decomposition provides a powerful method to look into the different processes behind a given time series data, and provides a way to separate short time-scale events from a general trend. We present a free software implementation of EMD, EEMD and CEEMDAN and give an overview of the EMD methodology and the algorithms used in the decomposition. We release our implementation, libeemd, with the aim of providing a user-friendly, fast, stable, well-documented and easily extensible EEMD library for anyone interested in using (E)EMD in the analysis of time series data. While written in C for numerical efficiency, our implementation includes interfaces to the Python and R languages, and interfaces to other languages are straightforward.

  • 15.
    Lyhagen, Johan
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Information Science.
    Identification of the order of a fractionally differenced ARMA model1999In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 14, no 2, p. 161-169Article in journal (Refereed)
    Abstract [en]

    Long term dependence in time series can be modelled by fractionally integrated ARMA (ARFIMA) models. For an ARFIMA process it is however impossible to identify the order of the short memory polynomials by inspection of the autocorrelation and partial autocorrelation functions. Instead information criteria such as AIC, BIC and HQIC are used to identify the order. This paper investigates the performance of the three information criteria when identifying the order in an ARFIMA model. The impression is that BIC outperforms AIC and HQIC, at least for the ARFIMA models used in this simulation. The overall performance of the information criteria, however, is poor for mixtures of AR and MA processes. Introducing long memory increases the likelihood of identifying the correct orders.

  • 16.
    Magnusson, Måns
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. Aalto University, Espoo, Finland.
    Jonsson, Leif
    Ericsson AB, Stockholm, Sweden.
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. Stockholm University, Stockholm, Sweden.
    DOLDA: a regularized supervised topic model for high-dimensional multi-class regression2019In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658Article in journal (Refereed)
    Abstract [en]

    Generating user interpretable multi-class predictions in data-rich environments with many classes and explanatory covariates is a daunting task. We introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervised topic model for multi-class classification that can handle many classes as well as many covariates. To handle many classes we use the recently proposed Diagonal Orthant probit model (Johndrow et al., in: Proceedings of the sixteenth international conference on artificial intelligence and statistics, 2013) together with an efficient Horseshoe prior for variable selection/shrinkage (Carvalho et al. in Biometrika 97:465–480, 2010). We propose a computationally efficient parallel Gibbs sampler for the new model. An important advantage of DOLDA is that learned topics are directly connected to individual classes without the need for a reference class. We evaluate the model’s predictive accuracy and scalability, and demonstrate DOLDA’s advantage in interpreting the generated predictions.

  • 17. Nyman, Henrik
    et al.
    Pensar, Johan
    Koski, Timo
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Corander, Jukka
    Context-specific independence in graphical log-linear models2016In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 31, no 4, p. 1493-1512Article in journal (Refereed)
    Abstract [en]

    Log-linear models are the popular workhorses of analyzing contingency tables. A log-linear parameterization of an interaction model can be more expressive than a direct parameterization based on probabilities, leading to a powerful way of defining restrictions derived from marginal, conditional and context-specific independence. However, parameter estimation is often simpler under a direct parameterization, provided that the model enjoys certain decomposability properties. Here we introduce a cyclical projection algorithm for obtaining maximum likelihood estimates of log-linear parameters under an arbitrary context-specific graphical log-linear model, which needs not satisfy criteria of decomposability. We illustrate that lifting the restriction of decomposability makes the models more expressive, such that additional context-specific independencies embedded in real data can be identified. It is also shown how a context-specific graphical model can correspond to a non-hierarchical log-linear parameterization with a concise interpretation. This observation can pave way to further development of non-hierarchical log-linear models, which have been largely neglected due to their believed lack of interpretability.

  • 18.
    Sjöstedt, Sara
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Barrlund, Anders
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    A computational method for estimating continuum factor models.1997In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 12, p. 481-495Article in journal (Refereed)
  • 19.
    Sjöstedt, Sara
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Barrlund, Anders
    A computational method for estimating continuum factor models1993In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 12, p. 481-495Article in journal (Refereed)
  • 20.
    Söderkvist, Inge
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    On algorithms for generalized least squares problems with ill-conditioned covariance matrices1996In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 11, p. 303-313Article in journal (Refereed)
    Abstract [en]

    The problem of computing estimates of parameters in linear models with additive noise and known ill-conditioned covariance matrix is considered. Some of the mostly used algorithms for solving the corresponding generalized least squares problem are discussed and complimentary numerical properties of the algorithms are exemplified. An algorithm, based on recent techniques for solving weighted and constrained least squares problems, is proposed. This algorithm handles both exact and diffuse information in an uniform framework and it shows to have superior numerical properties, compared to other frequently used algorithms, on some given examples.

  • 21.
    Telaar, Anna
    et al.
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Nürnberg, Gerd
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Biomarker discovery: classification using pooled samples2013In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 28, no 1, p. 67-106Article in journal (Refereed)
    Abstract [en]

    RNA-sample pooling is sometimes inevitable, but should be avoided in classification tasks like biomarker studies. Our simulation framework investigates a two-class classification study based on gene expression profiles to point out howstrong the outcomes of single sample designs differ to those of pooling designs. The results show how the effects of pooling depend on pool size, discriminating pattern, number of informative features and the statistical learning method used (support vector machines with linear and radial kernel, random forest (RF), linear discriminant analysis, powered partial least squares discriminant analysis (PPLS-DA) and partial least squares discriminant analysis (PLS-DA)). As a measure for the pooling effect, we consider prediction error (PE) and the coincidence of important feature sets for classification based on PLS-DA, PPLS-DAand RF. In general, PPLS-DAand PLS-DAshow constant PE with increasing pool size and low PE for patterns for which the convex hull of one class is not a cover of the other class. The coincidence of important feature sets is larger for PLS-DA and PPLS-DA as it is for RF. RF shows the best results for patterns in which the convex hull of one class is a cover of the other class, but these depend strongly on the pool size. We complete the PE results with experimental data whichwe pool artificially. The PE of PPLS-DAand PLS-DAare again least influenced by pooling and are low. Additionally, we show under which assumption the PLS-DA loading weights, as a measure for importance of features regarding classification, are equal for the different designs.

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