fm.smn.cr: Censored linear mixture regression models

In CensMixReg: Censored Linear Mixture Regression Models

Description

Performs a Finite Mixture Censored (FM-CR) using using EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters.

Usage

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)

Arguments

cc	Vector of censoring indicators. For each observation: 0 if non-censored, 1 if censored.
y	Vector of responses in case of right censoring.
x1	Matrix or vector of covariates.
Abetas	Parameters of vector regression dimension (p + 1) include intercept
medj	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)
sigma2	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)
pii	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)
nu	Initial value for the EM algorithm, nu it's degrees of freedom. Value of one size 1 (If Student's t)
g	Numbers of components
family	"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
error	define the stopping criterion of the algorithm
iter.max	the maximum number of iterations of the EM algorithm
aitken	Aitken acceleration: TRUE or FALSE.

Author(s)

Luis Benites Sanchez [e-mail], Victor Hugo Lachos [e-mail], Edgar J. Lopez Moreno [e-mail]

References

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.

See Also

fm.smn.cr,wage.rates

Examples

#See examples for the CensMixReg function linked above.

Example output

CensMixReg documentation built on May 2, 2019, 9:11 a.m.