This package contains a principal function that performs to estimate the parameters of a regression model considering an error that follows a finite mixture of Scale mixture of normal distributions, using an analytically simple and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters. Also contains a function for estimate the parameters of a censored linear models of finite mixture multivariate Student-t and Normal distributions.
| Package: | CensMixReg |
| Type: | Package |
| Version: | 1.5 |
| Date: | 2017-07-26 |
| License: | GPL (>=2) |
Benites, L., Lachos, V.H., Cabral, C.R.B. (2015). Robust Regression Modeling for Censored Data Based on Mixtures of Student-t Distributions. Technical Report 5, Universidade Estadual de Campinas. http://www.ime.unicamp.br/sites/default/files/rp05-15.pdf
Benites, L., Lachos, V.H., H. Bolfarine (2017). Robust Regression Modeling of Censored Databased on Mixtures of Scale Mixtures of Normal Distributions. Technical Report 1, University of Connecticut.
Karlsson, M. & Laitila, T. (2014). Finite mixture modeling of censored regression models. Statistical papers, 55(3), 627-642.
Massuia, M. B., Cabral, C. R. B., Matos, L. A. & Lachos, V. H. (2014). Influence diagnostics for student-t censored linear regression models. Statistics, (ahead-of-print), 1-21.
Arellano-Valle, R., Castro, L., Gonzalez-Farias, G. & Munoz-Gajardo, K. (2012). Student-t censored regression model: properties and inference. Statistical Methods & Applications, 21, 453-473.
Garay, A. M., Lachos, V. H., Bolfarine, H. & Cabral, C. R. (2015). Linear censored regression models with scale mixtures of normal distributions. Statistical Papers, pages 1-32.
# NOT RUN {
#See examples for the CensMixReg function linked above.
# }
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