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

In MPLikelihoodWB: Modified Profile Likelihood Estimation for Weibull Shape and Regression Parameters

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).

Author(s)

Mazharul Islam and Hasinur Rahaman Khan

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

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)

MPLikelihoodWB documentation built on May 2, 2019, 10:25 a.m.