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 Author:
 Ueckert, Sebastian; Mentré, France
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 203219
 ISSN:
 01679473
 Subject:
 algorithms; experimental design; group effect; models; variance
 Abstract:
 ... The design of experiments for discrete mixed effect models is challenging due to the unavailability of a closedform expression for the Fisher information matrix (FIM), on which most optimality criteria depend. Existing approaches for the computation of the FIM for those models are all based on approximations of the likelihood. A new method is presented which is based on derivatives of the exact c ...
 DOI:
 10.1016/j.csda.2016.10.011

http://dx.doi.org/10.1016/j.csda.2016.10.011
 Author:
 Ouyang, Ming; Yan, Xiaodong; Chen, Ji; Tang, Niansheng; Song, Xinyuan
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 102115
 ISSN:
 01679473
 Subject:
 Bayesian theory; bone density; statistical inference; statistical models; structural equation modeling
 Abstract:
 ... The authors develop a Bayesian local influence method for semiparametric structural equation models. The effects of minor perturbations to individual observations, the prior distributions of parameters, and the sampling distribution on the statistical inference are assessed with various perturbation schemes. A Bayesian perturbation manifold is constructed to characterize such perturbation schemes. ...
 DOI:
 10.1016/j.csda.2017.01.007

http://dx.doi.org/10.1016/j.csda.2017.01.007
 Author:
 Zhu, Hongxiao; Morris, Jeffrey S.; Wei, Fengrong; Cox, Dennis D.
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 88101
 ISSN:
 01679473
 Subject:
 Bayesian theory; biomarkers; discriminant analysis; experimental design; fluorescence emission spectroscopy; models; principal component analysis; prognosis; regression analysis
 Abstract:
 ... Many scientific studies measure different types of highdimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motiva ...
 DOI:
 10.1016/j.csda.2017.02.004

http://dx.doi.org/10.1016/j.csda.2017.02.004
 Author:
 Leung, Andy; Yohai, Victor; Zamar, Ruben
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 5976
 ISSN:
 01679473
 Subject:
 multivariate analysis
 Abstract:
 ... Real data may contain both cellwise outliers and casewise outliers. There is a vast literature on robust estimation for casewise outliers, but only a scant literature for cellwise outliers and almost none for both types of outliers. Estimation of multivariate location and scatter matrix is a corner stone in multivariate data analysis. A twostep approach was recently proposed to perform robust est ...
 DOI:
 10.1016/j.csda.2017.02.007

http://dx.doi.org/10.1016/j.csda.2017.02.007
 Author:
 Maire, Florian; Moulines, Eric; Lefebvre, Sidonie
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 2747
 ISSN:
 01679473
 Subject:
 Bayesian theory; algorithms; deformation; models
 Abstract:
 ... A novel approach to perform unsupervised sequential learning for functional data is proposed. The goal is to extract reference shapes (referred to as templates) from noisy, deformed and censored realizations of curves and images. The proposed model generalizes the Bayesian dense deformable template model, a hierarchical model in which the template is the function to be estimated and the deformatio ...
 DOI:
 10.1016/j.csda.2017.01.006

http://dx.doi.org/10.1016/j.csda.2017.01.006
 Author:
 Shin, Seung Jun; Artemiou, Andreas
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 4858
 ISSN:
 01679473
 Subject:
 regression analysis
 Abstract:
 ... Sufficient dimension reduction (SDR) is a successive tool for reducing the dimensionality of predictors by finding the central subspace, a minimal subspace of predictors that preserves all the regression information. When predictor dimension is large, it is often assumed that only a small number of predictors is informative. In this regard, sparse SDR is desired to achieve variable selection and d ...
 DOI:
 10.1016/j.csda.2016.12.003

http://dx.doi.org/10.1016/j.csda.2016.12.003
 Author:
 Alam, M. Iftakhar; Bogacka, Barbara; Coad, D. Stephen
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 183202
 ISSN:
 01679473
 Subject:
 blood; clinical trials; dose response; drugs; patients; pharmacokinetics; probability; toxicity
 Abstract:
 ... A new statistical method is introduced for dose finding in phase IB/IIA trials, which, along with efficacy and toxicity as endpoints, also considers pharmacokinetic information in the doseselection procedure. Following the assignment of a current best dose to a cohort of patients, the concentration of a drug in the blood is measured at the locally Doptimal time points. The dose–response outcomes ...
 DOI:
 10.1016/j.csda.2017.02.009

http://dx.doi.org/10.1016/j.csda.2017.02.009
 Author:
 Kouritzin, Michael A.
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 145165
 ISSN:
 01679473
 Subject:
 Bayesian theory; algorithms; filters; progeny
 Abstract:
 ... A class of discretetime branching particle filters is introduced with individual resampling: If there are Nn particles alive at time n, N0=N, an≤1≤bn, L̂n+1i is the current unnormalized importance weight for particle i and An+1=1N∑i=1NnL̂n+1i, then weight is preserved when L̂n+1i∈(anAn+1,bnAn+1). Otherwise, ⌊L̂n+1iAn+1⌋+ρni offspring are produced and assigned weight An+1, where ρni is a Bernoulli ...
 DOI:
 10.1016/j.csda.2017.02.003

http://dx.doi.org/10.1016/j.csda.2017.02.003
 Author:
 Gong, Joonho; Kim, Hyunjoong
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 113
 ISSN:
 01679473
 Subject:
 data collection; models
 Abstract:
 ... Imbalance data are defined as a dataset whose proportion of classes is severely skewed. Classification performance of existing models tends to deteriorate due to class distribution imbalance. In addition, overrepresentation by majority classes prevents a classifier from paying attention to minority classes, which are generally more interesting. An effective ensemble classification method called R ...
 DOI:
 10.1016/j.csda.2017.01.005

http://dx.doi.org/10.1016/j.csda.2017.01.005
 Author:
 Smucler, Ezequiel; Yohai, Victor J.
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 116130
 ISSN:
 01679473
 Subject:
 data collection; models; regression analysis
 Abstract:
 ... Penalized regression estimators are popular tools for the analysis of sparse and highdimensional models. However, penalized regression estimators defined using an unbounded loss function can be very sensitive to the presence of outlying observations, especially to high leverage outliers. The robust and asymptotic properties of ℓ1penalized MMestimators and MMestimators with an adaptive ℓ1 penal ...
 DOI:
 10.1016/j.csda.2017.02.002

http://dx.doi.org/10.1016/j.csda.2017.02.002
 Author:
 Belalia, Mohamed; Bouezmarni, Taoufik; Leblanc, Alexandre
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 166182
 ISSN:
 01679473
 Subject:
 variance
 Abstract:
 ... In a variety of statistical problems, estimation of the conditional distribution function remains a challenge. To this end, a twostage Bernstein estimator for conditional distribution functions is introduced. The method consists in smoothing a firststage Nadaraya–Watson or local linear estimator by constructing its Bernstein polynomial. Some asymptotic properties of the proposed estimator are de ...
 DOI:
 10.1016/j.csda.2017.02.005

http://dx.doi.org/10.1016/j.csda.2017.02.005
 Author:
 Qian, Lianfen; Wang, Suojin
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 7787
 ISSN:
 01679473
 Subject:
 linear models
 Abstract:
 ... In analyzing longitudinal data, withinsubject correlations are a major factor that affects statistical efficiency. Working with a partially linear model for longitudinal data, a subjectwise empirical likelihood based method that takes the withinsubject correlations into consideration is proposed to estimate the model parameters. A nonparametric version of the Wilks Theorem for the limiting dist ...
 DOI:
 10.1016/j.csda.2017.02.001

http://dx.doi.org/10.1016/j.csda.2017.02.001
 Author:
 AlNajjar, Elias; Adragni, Kofi P.
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 131144
 ISSN:
 01679473
 Subject:
 data collection; gender; models
 Abstract:
 ... Most methodologies for sufficient dimension reduction (SDR) in regression are limited to continuous predictors, although many data sets do contain both continuous and categorical variables. Application of these methods to regressions that include qualitative predictors such as gender or species may be inappropriate. Regressions that include a set of qualitative predictors W in addition to a vector ...
 DOI:
 10.1016/j.csda.2017.02.008

http://dx.doi.org/10.1016/j.csda.2017.02.008
 Author:
 Hu, Hao; Yao, Weixin; Wu, Yichao
 Source:
 Computational statistics & data analysis 2017 v.111 pp. 1426
 ISSN:
 01679473
 Subject:
 algorithms; methodology; models; regression analysis
 Abstract:
 ... Finite mixture of regression (FMR) models can be reformulated as incomplete data problems and they can be estimated via the expectation–maximization (EM) algorithm. The main drawback is the strong parametric assumption such as FMR models with normal distributed residuals. The estimation might be biased if the model is misspecified. To relax the parametric assumption about the component error densi ...
 DOI:
 10.1016/j.csda.2017.01.004

http://dx.doi.org/10.1016/j.csda.2017.01.004