MPLikelihoodWB (version 1.1)

LX.mat.weibull: Compensating factor for a possible mathematical disturbance

Description

Matrix as a component of modifying part of regression parameters :compensating factor for a possible mathematical disturbance

Usage

LX.mat.weibull(Y, X, sigma, phi, delta, whc)

Arguments

Y

log of Weibull distributed failure times

X

covariate matrix

sigma

given value of scale parameter of extreme value distribution

phi

given values of regression parameters of extreme value distribution

delta

Censoring status, coded as 0(censored observation) and 1(uncersored observation) binary integer variable

whc

Set position of regression parameter of interest corresponding predefined covariate matrix. It will take integer value from 1 to number of regression parameters.

Value

Matrix of dimension n*n (n is number of regression parameter).

References

Barndorff-Nielsen (1980). Conditionality resolutions. Biometrika, 67(2) : 293-310.

Barndorff-Nielsen (1983). On a formula for the distribution of the maximum likelihood estimator. Biometrika, 70(2) : 343-365.

Khan M. H. R. and Shaw J. E. H (2016). Variable selection for survival data with a class of adaptive elastic net techniques. Statistics and Computing, 26(3): 725-741.

Islam, M. M., Khan, M. H. R. and Hawlader T. (2015). Modified profile likelihood estimation for the weibull regression models in survival analysis. Submitted.

See Also

J.inf.weibul

Examples

# NOT RUN {
dat <- data.weibull(n = 20, shape=2, regco=c(2,1.5,3,2.5))

par=c(1,1,1,1,1,1)

LX.mat.weibull(Y=log(dat$ftime),X=model.matrix(ftime~x1+x2+x3+x4,data=dat),
sigma=2,phi=matrix(par[-1],ncol=1),delta=dat$delta,whc=2)

par=c(1,1,1)

LX.mat.weibull(Y=log(dat$ftime),X=model.matrix(ftime~x1,data=dat),sigma=2,
phi=matrix(par[-1],ncol=1),delta=dat$delta,whc=2)
# }