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 Author:
 AlvarezIglesias, Alberto; Hinde, John; Ferguson, John; Newell, John
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 90102
 ISSN:
 01679473
 Subject:
 algorithms; linear models
 Abstract:
 ... Treebased methods are a nonparametric modelling strategy that can be used in combination with generalized linear models or Cox proportional hazards models, mostly at an exploratory stage. Their popularity is mainly due to the simplicity of the technique along with the ease in which the resulting model can be interpreted. Variable selection bias from variables with many possible splits or missing ...
 DOI:
 10.1016/j.csda.2016.08.011

http://dx.doi.org/10.1016/j.csda.2016.08.011
 Author:
 McGinnity, K.; Varbanov, R.; Chicken, E.
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 127137
 ISSN:
 01679473
 Subject:
 wavelet
 Abstract:
 ... Wavelet thresholding generally assumes independent, identically distributed normal errors when estimating functions in a nonparametric regression setting. VisuShrink and SureShrink are just two of the many common thresholding methods based on this assumption. When the errors are not normally distributed, however, few methods have been proposed. A distributionfree method for thresholding wavelet c ...
 DOI:
 10.1016/j.csda.2016.09.010

http://dx.doi.org/10.1016/j.csda.2016.09.010
 Author:
 Liu, Baisen; Wang, Liangliang; Cao, Jiguo
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 153164
 ISSN:
 01679473
 Subject:
 algorithms; atmospheric precipitation; mortality; ozone; regression analysis; statistical models; temperature; variance
 Abstract:
 ... A new functional linear mixed model is proposed to investigate the impact of functional predictors on a scalar response when repeated measurements are available on multiple subjects. The advantage of the proposed model is that under the proposed model, each subject has both individual scalar covariate effects and individual functional effects over time, while it shares the common population scalar ...
 DOI:
 10.1016/j.csda.2016.09.009

http://dx.doi.org/10.1016/j.csda.2016.09.009
 Author:
 Li, J.; Nott, D.J.; Fan, Y.; Sisson, S.A.
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 7789
 ISSN:
 01679473
 Subject:
 Bayesian theory; algorithms; models
 Abstract:
 ... Approximate Bayesian computation (ABC) refers to a family of inference methods used in the Bayesian analysis of complex models where evaluation of the likelihood is difficult. Conventional ABC methods often suffer from the curse of dimensionality, and a marginal adjustment strategy was recently introduced in the literature to improve the performance of ABC algorithms in highdimensional problems. ...
 DOI:
 10.1016/j.csda.2016.07.005

http://dx.doi.org/10.1016/j.csda.2016.07.005
 Author:
 Gramacki, Artur; Gramacki, Jarosław
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 2745
 ISSN:
 01679473
 Subject:
 mathematical models; motivation
 Abstract:
 ... The performance of multivariate kernel density estimation (KDE) depends strongly on the choice of bandwidth matrix. The high computational cost required for its estimation provides a big motivation to develop fast and accurate methods. One of such methods is based on the Fast Fourier Transform. However, the currently available implementation works very well only for the univariate KDE and its mult ...
 DOI:
 10.1016/j.csda.2016.09.001

http://dx.doi.org/10.1016/j.csda.2016.09.001
 Author:
 Kwon, Yongchan; Choi, YoungGeun; Park, Taesung; Ziegler, Andreas; Paik, Myunghee Cho
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 111
 ISSN:
 01679473
 Subject:
 equations; regression analysis; variance
 Abstract:
 ... Generalized estimating equations (GEE) proposed by Liang and Zeger (1986) yield a consistent estimator for the regression parameter without correctly specifying the correlation structure of the repeatedly measured outcomes. It is well known that the efficiency of regression coefficient estimator increases with correctly specified working correlation and thus unstructured correlation could be a goo ...
 DOI:
 10.1016/j.csda.2016.08.016

http://dx.doi.org/10.1016/j.csda.2016.08.016
 Author:
 Panagiotelis, Anastasios; Czado, Claudia; Joe, Harry; Stöber, Jakob
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 138152
 ISSN:
 01679473
 Subject:
 algorithms; models; probability; surveys; vines
 Abstract:
 ... Discrete vine copulas provide a flexible modeling framework for highdimensional data and have significant computational advantages over competing methods. A vinebased multivariate probability mass function is constructed from bivariate copula building blocks and univariate marginal distributions. However, even for a moderate number of variables, the number of alternative vine decompositions is v ...
 DOI:
 10.1016/j.csda.2016.09.007

http://dx.doi.org/10.1016/j.csda.2016.09.007
 Author:
 Wang, Dan; Tian, Lili
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 1226
 ISSN:
 01679473
 Subject:
 adults; algorithms; biomarkers; confidence interval; data collection; gene expression; gene expression regulation; microarray technology; probability distribution
 Abstract:
 ... Overlap coefficient (OVL), the proportion of overlap area between two probability distributions, is a direct measure of similarity between two distributions. It is useful in microarray analysis for the purpose of identifying differentially expressed biomarkers, especially when data follow multimodal distribution which cannot be transformed to normal. However, the inference methods about OVL are qu ...
 DOI:
 10.1016/j.csda.2016.08.013

http://dx.doi.org/10.1016/j.csda.2016.08.013
 Author:
 Bianco, Ana M.; Spano, Paula M.
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 4664
 ISSN:
 01679473
 Subject:
 models; regression analysis
 Abstract:
 ... In many applications of regression analysis, there are covariates that are measured with errors. A robust family of estimators of the parametric and nonparametric components of a structural partially linear errorsinvariables model is introduced. The proposed estimators are based on a threestep procedure where robust orthogonal regression estimators are combined with robust smoothing techniques. ...
 DOI:
 10.1016/j.csda.2016.09.002

http://dx.doi.org/10.1016/j.csda.2016.09.002
 Author:
 Schaarschmidt, Frank; Gerhard, Daniel; Vogel, Charlotte
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 6576
 ISSN:
 01679473
 Subject:
 Monte Carlo method; blood; computer software; confidence interval; toxicology
 Abstract:
 ... Multinomial data occur if the major outcome of an experiment is the classification of experimental units into more than two mutually exclusive categories. In experiments with several treatment groups, one may then be interested in multiple comparisons between the treatments w.r.t several definitions of odds between the multinomial proportions. Asymptotic methods are described for constructing simu ...
 DOI:
 10.1016/j.csda.2016.09.004

http://dx.doi.org/10.1016/j.csda.2016.09.004
 Author:
 Baek, Changryong; Davis, Richard A.; Pipiras, Vladas
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 103126
 ISSN:
 01679473
 Subject:
 models; time series analysis
 Abstract:
 ... Seasonal and periodic vector autoregressions are two common approaches to modeling vector time series exhibiting cyclical variations. The total number of parameters in these models increases rapidly with the dimension and order of the model, making it difficult to interpret the model and questioning the stability of the parameter estimates. To address these and other issues, two methodologies for ...
 DOI:
 10.1016/j.csda.2016.09.005

http://dx.doi.org/10.1016/j.csda.2016.09.005