Performs a Finite Mixture Censored (FM-CR) using using EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters.
fm.smn.cr(cc, y, x1, Abetas = NULL, medj = NULL, sigma2 = NULL, pii = NULL,
nu=NULL, g = NULL, family = "Normal", error = 0.00001, iter.max = 100, aitken = TRUE)Vector of censoring indicators. For each observation: 0 if non-censored, 1 if censored.
Vector of responses in case of right censoring.
Matrix or vector of covariates.
Parameters of vector regression dimension \((p + 1)\) include intercept
Initial value for the EM algorithm. Each of them must be a vector of length g.(the algorithm considers the number of components to be fitted based on the size of these vectors)
Initial value for the EM algorithm. Each of them must be a vector of length g.(the algorithm considers the number of components to be adjusted based on the size of these vectors)
Initial value for the EM algorithm. Each of them must be a vector of length g.(the algorithm considers the number of components to be adjusted based on the size of these vectors)
Initial value for the EM algorithm, nu it's degrees of freedom. Value of one size 1 (If Student's t)
Numbers of components
"T": fits a t-student regression mixture for censured data or "Normal": fits a Normal regression mixture censored data or "Slash": fits a Slash regression mixture censored data
define the stopping criterion of the algorithm
the maximum number of iterations of the EM algorithm
Aitken acceleration: TRUE or FALSE.
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
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.
Arellano-Valle, R. B., Castro, L., Gonzalez-Farias, G. & Munos Gajardo, K. (2012). Student-t censored regression model: properties and inference. Statistical Methods and Applications, 21(4), 453-473.
Dempster, A., Laird, N. & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B,39, 1-38.
Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing,10(4), 339-348.
Karlsson, M. & Laitila, T. (2014). Finite mixture modeling of censored regression models. Statistical Papers,55(3), 627-642.
Basso,R.M.,Lachos,V.H.,Cabral,C.R.B. & Ghosh,P. (2010). Robust mixture modeling based on scale mixtures of skew-normal distributions. Computational Statistics & Data Analysis, 54(12), 2926-2941.
Basford, K., Greenway, D.,McLachlan,G. & Peel,D. (1997). Standard errors of fitted component means of normal mixtures. Computational Statistics,12, 1-18.
# NOT RUN {
#See examples for the CensMixReg function linked above.
# }
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