• [+]
• [–]

Computational Statistics & Data Analysis, Volume 71

Volume 71, March 2014

Special Section: Advances in Mixture Models

• 
  Dankmar Böhning, Christian Hennig, Geoffrey J. McLachlan, Paul D. McNicholas:
  The 2nd special issue on advances in mixture models. 1-2

Special Issue Articles

• 
  Donatella Vicari, Marco Alfò:
  Model based clustering of customer choice data. 3-13
• 
  Norma Bargary, John Hinde, E. Holian:
  Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data. 14-29
• 
  Stijn Jaspers, Marc Aerts, Geert Verbeke, Pierre-Alexandre Beloeil:
  A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance. 30-42
• 
  Shu-Kay Ng, Geoffrey J. McLachlan:
  Mixture models for clustering multilevel growth trajectories. 43-51
• 
  Charles Bouveyron, Camille Brunet-Saumard:
  Model-based clustering of high-dimensional data: A review. 52-78
• 
  Daniela G. Calò, Angela Montanari, Cinzia Viroli:
  A hierarchical modeling approach for clustering probability density functions. 79-91
• 
  Julien Jacques, Cristian Preda:
  Model-based clustering for multivariate functional data. 92-106
• 
  Athanase Polymenis:
  A combined likelihood ratio/information ratio bootstrap technique for estimating the number of components in finite mixtures. 107-115
• 
  Weixin Yao, Yan Wei, Chun Yu:
  Robust mixture regression using the t-distribution. 116-127
• 
  Weixing Song, Weixin Yao, Yanru Xing:
  Robust mixture regression model fitting by Laplace distribution. 128-137
• 
  Giuliano Galimberti, Gabriele Soffritti:
  A multivariate linear regression analysis using finite mixtures of t distributions. 138-150
• 
  Hwa Kyung Lim, Wai Keung Li, Philip L. H. Yu:
  Zero-inflated Poisson regression mixture model. 151-158
• 
  Salvatore Ingrassia, Simona C. Minotti, Antonio Punzo:
  Model-based clustering via linear cluster-weighted models. 159-182
• 
  Tsung-I Lin:
  Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition. 183-195
• 
  Irene Vrbik, Paul D. McNicholas:
  Parsimonious skew mixture models for model-based clustering and classification. 196-210
• 
  Mary Lesperance, Rabih Saab, John Neuhaus:
  Nonparametric estimation of the mixing distribution in logistic regression mixed models with random intercepts and slopes. 211-219
• 
  Zhenqiu (Laura) Lu, Zhiyong Zhang:
  Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application. 220-240
• 
  Shirley Pledger, Richard Arnold:
  Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection. 241-261
• 
  Silvia Bacci, Francesco Bartolucci:
  Mixtures of equispaced normal distributions and their use for testing symmetry with univariate data. 262-272

Special Section: Bayesian Computing, Methods and Applications

• 
  Cathy W. S. Chen, David B. Dunson, Sylvia Frühwirth-Schnatter, Stephen G. Walker:
  Special issue on Bayesian computing, methods and applications. 273

Special Issue Articles

• 
  Carlos A. Abanto-Valle, Dipak K. Dey:
  State space mixed models for binary responses with scale mixture of normal distributions links. 274-287
• 
  David Azriel:
  Optimal sequential designs in phase I studies. 288-297
• 
  Stelios D. Bekiros, Alessia Paccagnini:
  Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models. 298-323
• 
  Bingshu E. Chen, Wenyu Jiang, Dongsheng Tu:
  A hierarchical Bayes model for biomarker subset effects in clinical trials. 324-334
• 
  Yuhui Chen, Timothy E. Hanson:
  Bayesian nonparametric k-sample tests for censored and uncensored data. 335-346
• 
  S. McKay Curtis, Sayantan Banerjee, Subhashis Ghosal:
  Fast Bayesian model assessment for nonparametric additive regression. 347-358
• 
  Stefano Grassi, Tommaso Proietti:
  Characterising economic trends by Bayesian stochastic model specification search. 359-374
• 
  Mayetri Gupta:
  An evolutionary Monte Carlo algorithm for Bayesian block clustering of data matrices. 375-391
• 
  Sarah E. Heaps, Richard J. Boys, Malcolm Farrow:
  Computation of marginal likelihoods with data-dependent support for latent variables. 392-401
• 
  Ick-Hoon Jin, Faming Liang:
  Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants. 402-416
• 
  Antonio Lijoi, Bernardo Nipoti, Igor Prünster:
  Dependent mixture models: Clustering and borrowing information. 417-433
• 
  Trevelyan J. McKinley, Joshua V. Ross, Rob Deardon, Alex R. Cook:
  Simulation-based Bayesian inference for epidemic models. 434-447
• 
  Joris Mulder:
  Prior adjusted default Bayes factors for testing (in)equality constrained hypotheses. 448-463
• 
  Lizbeth Naranjo, Jacinto Martín, Carlos J. Pérez:
  Bayesian binary regression with exponential power link. 464-476
• 
  Luis E. Nieto-Barajas:
  Bayesian semiparametric analysis of short- and long-term hazard ratios with covariates. 477-490
• 
  David J. Nott, Lucy A. Marshall, Mark Fielding, Shie-Yui Liong:
  Mixtures of experts for understanding model discrepancy in dynamic computer models. 491-505
• 
  Gabriel Nuñez-Antonio, Eduardo Gutiérrez-Peña:
  A Bayesian model for longitudinal circular data based on the projected normal distribution. 506-519
• 
  Yu Qiu, Daniel J. Nordman, Stephen B. Vardeman:
  One-sample Bayes inference for symmetric distributions of 3-D rotations. 520-529
• 
  Tuomas Rajala, Antti Penttinen:
  Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range. 530-541
• 
  Anne Sabourin, Philippe Naveau:
  Bayesian Dirichlet mixture model for multivariate extremes: A re-parametrization. 542-567
• 
  Mike K. P. So, Raymond K. S. Chan:
  Bayesian analysis of tail asymmetry based on a threshold extreme value model. 568-587
• 
  Charlotte Soneson, Magnus Fontes:
  Incorporation of gene exchangeabilities improves the reproducibility of gene set rankings. 588-598
• 
  Luigi Spezia, S. L. Cooksley, M. J. Brewer, D. Donnelly, A. Tree:
  Modelling species abundance in a river by Negative Binomial hidden Markov models. 599-614
• 
  Frank van der Meulen, Moritz Schauer, Harry van Zanten:
  Reversible jump MCMC for nonparametric drift estimation for diffusion processes. 615-632
• 
  Lichun Wang, Radhey S. Singh:
  Linear Bayes estimator for the two-parameter exponential family under type II censoring. 633-642
• 
  Gentry White, Michael D. Porter:
  GPU accelerated MCMC for modeling terrorist activity. 643-651

Special Section: Model Selection and High Dimensional Data Reduction

• 
  S. Ejaz Ahmed, Gerda Claeskens, Hidetoshi Shimodaira, Stefan Van Aelst:
  Special issue on Model Selection and High Dimensional Data Reduction. 652-653

Special Issue Articles

• 
  Rudolf Beran:
  Hypercube estimators: Penalized least squares, submodel selection, and numerical stability. 654-666
• 
  A. Blommaert, Niel Hens, Philippe Beutels:
  Data mining for longitudinal data under multicollinearity and time dependence using penalized generalized estimating equations. 667-680
• 
  David Dernoncourt, Blaise Hanczar, Jean-Daniel Zucker:
  Analysis of feature selection stability on high dimension and small sample data. 681-693
• 
  Peter Hall, Jing-Hao Xue:
  On selecting interacting features from high-dimensional data. 694-708
• 
  Luís F. Martins, Vasco J. Gabriel:
  Linear instrumental variables model averaging estimation. 709-724
• 
  Jan Mielniczuk, Pawel Teisseyre:
  Using random subspace method for prediction and variable importance assessment in linear regression. 725-742
• 
  Mathilde Mougeot, Dominique Picard, Karine Tribouley:
  LOL selection in high dimension. 743-757
• 
  Michael Schomaker, Christian Heumann:
  Model selection and model averaging after multiple imputation. 758-770
• 
  Martin Vincent, Niels Richard Hansen:
  Sparse group lasso and high dimensional multinomial classification. 771-786

Special Section: IMPRECISION IN STATISTICAL DATA ANALYSIS

• 
  Ana Colubi, Thierry Denoeux:
  Special issue on imprecision in statistical data analysis. 787-788

Special Issue Articles

• 
  Joaquín Abellán, Rebecca M. Baker, Frank P. A. Coolen, Richard J. Crossman, Andrés R. Masegosa:
  Classification with decision trees from a nonparametric predictive inference perspective. 789-802
• 
  Christine Choirat, Raffaello Seri:
  Bootstrap confidence sets for the Aumann mean of a random closed set. 803-817
• 
  Giorgio Corani, Alessandro Antonucci:
  Credal ensembles of classifiers. 818-831
• 
  Benoît Frénay, Gauthier Doquire, Michel Verleysen:
  Estimating mutual information for feature selection in the presence of label noise. 832-848
• 
  Jan Hannig, Randy C. S. Lai, Thomas C. M. Lee:
  Computational issues of generalized fiducial inference. 849-858
• 
  Jesús López-Fidalgo, María Jesús Rivas-López:
  Optimal experimental designs for partial likelihood information. 859-867
• 
  Ignacio Montes, Enrique Miranda, Susana Montes:
  Stochastic dominance with imprecise information. 868-886

Spectial Section: STATISTICAL ALGORITHMS AND SOFTWARE IN R

• 
  Peter Filzmoser, Cristian Gatu, Achim Zeileis:
  Special issue on statistical algorithms and software in R. 887-888

Special Issue Articles

• 
  Thomas W. Yee:
  Reduced-rank vector generalized linear models with two linear predictors. 889-902
• 
  Adriano Polpo, Cassio P. de Campos, Debajyoti Sinha, Stuart R. Lipsitz, Jianhang Lin:
  Transform both sides model: A parametric approach. 903-913
• 
  Giovanni Millo:
  Maximum likelihood estimation of spatially and serially correlated panels with random effects. 914-933
• 
  Nina Golyandina, Anton I. Korobeynikov:
  Basic Singular Spectrum Analysis and forecasting with R. 934-954
• 
  William H. Asquith:
  Parameter estimation for the 4-parameter Asymmetric Exponential Power distribution by the method of L-moments using R. 955-970
• 
  Francesco Bartolucci, Silvia Bacci, Michela Gnaldi:
  MultiLCIRT: An R package for multidimensional latent class item response models. 971-985
• 
  Manuel J. A. Eugster, Friedrich Leisch, Carolin Strobl:
  (Psycho-)analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithms. 986-1000
• 
  Liqun Wang, Chel Hee Lee:
  Discretization-based direct random sample generation. 1001-1010
• 
  Ching-Wei Cheng, Ying-Chao Hung, Narayanaswamy Balakrishnan:
  Generating beta random numbers and Dirichlet random vectors in R: The package rBeta2009. 1011-1020
• 
  Clément Chevalier, Victor Picheny, David Ginsbourger:
  KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging. 1021-1034
• 
  Victor Picheny, David Ginsbourger:
  Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package. 1035-1053
• 
  Dirk Eddelbuettel, Conrad Sanderson:
  RcppArmadillo: Accelerating R with high-performance C++ linear algebra. 1054-1063

Special Section: ALGORITHMS FOR DESIGN OF EXPERIMENTS

• 
  Steven Gilmour, Roger W. Payne:
  Special issue on algorithms for design of experiments. 1064-1065

Special Issue Articles

• 
  Haftom T. Abebe, Frans E. S. Tan, Gerard J. P. Van Breukelen, Martijn P. F. Berger:
  Bayesian D-optimal designs for the two parameter logistic mixed effects model. 1066-1076
• 
  B. Almohaimeed, Alexander N. Donev:
  Experimental designs for drug combination studies. 1077-1087
• 
  Alexis Boukouvalas, Dan Cornford, Milan Stehlík:
  Optimal design for correlated processes with input-dependent noise. 1088-1102
• 
  Holger Dette, Andrey Pepelyshev, Anatoly A. Zhigljavsky:
  'Nearly' universally optimal designs for models with correlated observations. 1103-1112
• 
  Norbert Gaffke, Ulrike Graßhoff, Rainer Schwabe:
  Algorithms for approximate linear regression design with application to a first order model with heteroscedasticity. 1113-1123
• 
  Stelios D. Georgiou, Stella Stylianou, Manohar Aggarwal:
  A class of composite designs for response surface methodology. 1124-1133
• 
  Janet D. Godolphin, H. R. Warren:
  An efficient procedure for the avoidance of disconnected incomplete block designs. 1134-1146
• 
  Alex J. Gutman, Edward D. White, Dennis K. J. Lin, Raymond R. Hill:
  Augmenting supersaturated designs with Bayesian D-optimality. 1147-1158
• 
  Radoslav Harman, Lenka Filová:
  Computing efficient exact designs of experiments using integer quadratic programming. 1159-1167
• 
  S. Loeza-Serrano, Alexander N. Donev:
  Construction of experimental designs for estimating variance components. 1168-1177
• 
  Lu Lu, Christine M. Anderson-Cook, Dennis K. J. Lin:
  Optimal designed experiments using a Pareto front search for focused preference of multiple objectives. 1178-1192
• 
  Francesco Sambo, Matteo Borrotti, Kalliopi Mylona:
  A coordinate-exchange two-phase local search algorithm for the D- and I-optimal designs of split-plot experiments. 1193-1207
• 
  Chiara Tommasi, Juan M. Rodríguez-Díaz, M. Teresa Santos-Martín:
  Integral approximations for computing optimum designs in random effects logistic regression models. 1208-1220