# 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

 `1` ```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.

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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) ```