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 Author:
 Gard, Charlotte C.; Brown, Elizabeth R.
 Source:
 Computational statistics & data analysis 2015 v.87 pp. 7383
 ISSN:
 01679473
 Subject:
 Bayesian theory; Markov chain; algorithms; models; probability distribution
 Abstract:
 ... A Bayesian hierarchical model for simultaneously estimating and partitioning probability density functions is presented. Individual density functions are flexibly modeled using Bernstein densities, which are mixtures of beta densities whose parameters depend only on the number of mixture components. A prior distribution is placed on the number of mixture components, and the mixture weights are exp ...
 DOI:
 10.1016/j.csda.2015.01.016

http://dx.doi.org/10.1016/j.csda.2015.01.016
 Author:
 BarrancoChamorro, I.; JiménezGamero, M.D.; MayorGallego, J.A.; MorenoRebollo, J.L.
 Source:
 Computational statistics & data analysis 2015 v.87 pp. 1833
 ISSN:
 01679473
 Subject:
 data collection; diagnostic techniques; models; regression analysis; surveys; system optimization
 Abstract:
 ... The penalized calibration technique in survey sampling combines usual calibration and soft calibration by introducing a penalty term. Certain relevant estimates in survey sampling can be considered as penalized calibration estimates obtained as particular cases from an optimization problem with a common basic structure. In this framework, a case deletion diagnostic is proposed for a class of penal ...
 DOI:
 10.1016/j.csda.2015.01.004

http://dx.doi.org/10.1016/j.csda.2015.01.004
 Author:
 Yu, Dalei; Bai, Peng; Ding, Chang
 Source:
 Computational statistics & data analysis 2015 v.87 pp. 116135
 ISSN:
 01679473
 Subject:
 crime; models; regression analysis; uncertainty
 Abstract:
 ... Under flexible distributional assumptions, the adjusted quasimaximum likelihood (adqml) estimator for mixed regressive, spatial autoregressive model is studied in this paper. The proposed estimation method accommodates the extra uncertainty introduced by the unknown regression coefficients. Moreover, the explicit expressions of theoretical/feasible secondorderbias of the adqml estimator are der ...
 DOI:
 10.1016/j.csda.2015.02.003

http://dx.doi.org/10.1016/j.csda.2015.02.003
 Author:
 Niu, Yi; Peng, Yingwei
 Source:
 Computational statistics & data analysis 2015 v.87 pp. 4656
 ISSN:
 01679473
 Subject:
 equations; kidney diseases; kidneys; models
 Abstract:
 ... Clustered failure time data often arise in biomedical studies and a marginal regression modeling approach is often preferred to avoid assumption on the dependence structure within clusters. A novel estimating equation approach is proposed based on a semiparametric marginal proportional hazards model to take the correlation within clusters into account. Different from the traditional marginal metho ...
 DOI:
 10.1016/j.csda.2015.01.012

http://dx.doi.org/10.1016/j.csda.2015.01.012
 Author:
 Vinué, Guillermo; Epifanio, Irene; Alemany, Sandra
 Source:
 Computational statistics & data analysis 2015 v.87 pp. 102115
 ISSN:
 01679473
 Subject:
 algorithms; data collection
 Abstract:
 ... The new concept archetypoids is introduced. Archetypoid analysis represents each observation in a dataset as a mixture of actual observations in the dataset, which are pure type or archetypoids. Unlike archetype analysis, archetypoids are real observations, not a mixture of observations. This is relevant when existing archetypal observations are needed, rather than fictitious ones. An algorithm is ...
 DOI:
 10.1016/j.csda.2015.01.018

http://dx.doi.org/10.1016/j.csda.2015.01.018
 Author:
 Klimova, Anna; Uhler, Caroline; Rudas, Tamás
 Source:
 Computational statistics & data analysis 2015 v.87 pp. 5772
 ISSN:
 01679473
 Subject:
 graphs; linear models
 Abstract:
 ... The concepts of faithfulness and strongfaithfulness are important for statistical learning of graphical models. Graphs are not sufficient for describing the association structure of a discrete distribution. Hypergraphs representing hierarchical loglinear models are considered instead, and the concept of parametric (strong)faithfulness with respect to a hypergraph is introduced. The strength of ...
 DOI:
 10.1016/j.csda.2015.01.017

http://dx.doi.org/10.1016/j.csda.2015.01.017
 Author:
 Yang, Zhao; Zhou, Ming
 Source:
 Computational statistics & data analysis 2015 v.87 pp. 117
 ISSN:
 01679473
 Subject:
 Human immunodeficiency virus; Monte Carlo method; algorithms; covariance; probability; variance
 Abstract:
 ... Motivated by the recent advances in the kappa statistic for the clustered physician–patients dichotomous data, we extend the development for the polytomous data. For the clustered physician–patients polytomous data, based on its special correlation and covariance structure, we propose a simple and efficient data generation algorithm, and develop a semiparametric variance estimator for the kappa s ...
 DOI:
 10.1016/j.csda.2015.01.007

http://dx.doi.org/10.1016/j.csda.2015.01.007
 Author:
 Tang, Yang; Browne, Ryan P.; McNicholas, Paul D.
 Source:
 Computational statistics & data analysis 2015 v.87 pp. 84101
 ISSN:
 01679473
 Subject:
 algorithms; models
 Abstract:
 ... A mixture of latent trait models with common slope parameters for modelbased clustering of highdimensional binary data, a data type for which few established methods exist, is proposed. Recent work on clustering of binary data, based on a ddimensional Gaussian latent variable, is extended by incorporating common factor analyzers. Accordingly, this approach facilitates a lowdimensional visual r ...
 DOI:
 10.1016/j.csda.2014.12.009

http://dx.doi.org/10.1016/j.csda.2014.12.009
 Author:
 Chen, Yurong; Feng, Yanqin; Sun, Jianguo
 Source:
 Computational statistics & data analysis 2015 v.87 pp. 3445
 ISSN:
 01679473
 Subject:
 models; regression analysis
 Abstract:
 ... In a biomedical study, it often occurs that some covariates of interest are not measured exactly and only some auxiliary information on them is available. In this case, a question of interest is how to make use of the available auxiliary information for statistical analysis. This paper discusses this problem in the context of regression analysis of multivariate current status failure time data ari ...
 DOI:
 10.1016/j.csda.2015.01.005

http://dx.doi.org/10.1016/j.csda.2015.01.005