CensMixReg swMATH ID: 21090 Software Authors: Lachos, Víctor H.; Moreno, Edgar J.López; Chen, Kun; Cabral, Celso Rômulo Barbosa Description: Finite mixture modeling of censored data using the multivariate student-t distribution. Finite mixture models have been widely used for the modeling and analysis of data from a heterogeneous population. Moreover, data of this kind can be subject to some upper and/or lower detection limits because of the restriction of experimental apparatus. Another complication arises when measures of each population depart significantly from normality, for instance, in the presence of heavy tails or atypical observations. For such data structures, we propose a robust model for censored data based on finite mixtures of multivariate Student-\(t\) distributions. This approach allows us to model data with great flexibility, accommodating multimodality, heavy tails and also skewness depending on the structure of the mixture components. We develop an analytically simple, yet efficient, EM-type algorithm for conducting maximum likelihood estimation of the parameters. The algorithm has closed-form expressions at the E-step that rely on formulas for the mean and variance of the multivariate truncated Student-\(t\) distributions. Further, a general information-based method for approximating the asymptotic covariance matrix of the estimators is also presented. Results obtained from the analysis of both simulated and real datasets are reported to demonstrate the effectiveness of the proposed methodology. The proposed algorithm and methods are implemented in the new R package CensMixReg. Homepage: https://cran.r-project.org/web/packages/CensMixReg/index.html Source Code: https://github.com/cran/CensMixReg Dependencies: R Keywords: censored data; detection limit; EM-type algorithms; finite mixture models; multivariate student-t Related Software: MomTrunc; tlmec; R; lmec; AS 136; mixsmsn; CensMFM; QSIMVN; mclust; TTmoment; tmvtnorm; mnormt; skewlmm; EMMIXcskew; sn; CRAN; Emmixuskew; Matlab; ClusterR; trimcluster Cited in: 14 Publications Standard Articles 2 Publications describing the Software, including 2 Publications in zbMATH Year Finite mixture of regression models for censored data based on scale mixtures of normal distributions. Zbl 1474.62259Zeller, Camila Borelli; Cabral, Celso Rômulo Barbosa; Lachos, Víctor Hugo; Benites, Luis 2019 Finite mixture modeling of censored data using the multivariate Student-\(t\) distribution. Zbl 1397.62221Lachos, Víctor H.; Moreno, Edgar J. López; Chen, Kun; Cabral, Celso Rômulo Barbosa 2017 all top 5 Cited by 23 Authors 8 Lachos Dávila, Víctor Hugo 5 Lin, Tsung I. 4 Wang, Wan-Lun 3 Cabral, Celso Rômulo Barbosa 3 Galarza, Christian E. 3 Matos, Larissa A. 2 Castro, Luis Mauricio 2 Dey, Dipak Kumar 2 Jamalizadeh, Ahad 2 Naderi, Mehrdad 1 Azzalini, Adelchi 1 Benites, Luis 1 Chen, Kun 1 de Alencar, Francisco H. C. 1 Fei, Yu 1 Hung, Wenliang 1 Mirfarah, Elham 1 Morales, Christian E. Galarza 1 Moreno, Edgar J. López 1 Prates, Marcos Oliveira 1 Tian, Guoliang 1 Zeller, Camila Borelli 1 Zhai, Yibo all top 5 Cited in 9 Serials 4 Journal of Multivariate Analysis 2 Computational Statistics and Data Analysis 2 Advances in Data Analysis and Classification. ADAC 1 Metrika 1 Computational Statistics 1 Test 1 Statistical Papers 1 Brazilian Journal of Probability and Statistics 1 Journal of Computational and Graphical Statistics Cited in 3 Fields 14 Statistics (62-XX) 3 Probability theory and stochastic processes (60-XX) 1 Astronomy and astrophysics (85-XX) Citations by Year