Shrinkage estimators for semiparametric regression model

SEMIPARAMETRIC ESTIMATORS IN COMPETING RISKS REGRESSION by

Stein Type Estimators for Disturbance Variance in Linear Regression Model

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Penalized Estimators in Cox Regression Model

The proportional hazard Cox regression models play a key role in analyzing censored survival data. We use penalized methods in high dimensional scenarios to achieve more efficient models. This article reviews the penalized Cox regression for some frequently used penalty functions. Analysis of medical data namely ”mgus2” confirms the penalized Cox regression performs better than the cox regressi...

A Semiparametric Regression Model for Panel Count Data: When Do Pseudo-likelihood Estimators Become Badly Inefficient?

We consider estimation in a particular semiparametric regression model for the mean of a counting process under the assumption of “panel count” data. The basic model assumption is that the conditional mean function of the counting process is of the form E{N(t)|Z} = exp(θ′Z)Λ(t) where Z is a vector of covariates and Λ is the baseline mean function. The “panel count” observation scheme involves o...

Generalized Ridge Regression Estimator in Semiparametric Regression Models

In the context of ridge regression, the estimation of ridge (shrinkage) parameter plays an important role in analyzing data. Many efforts have been put to develop skills and methods of computing shrinkage estimators for different full-parametric ridge regression approaches, using eigenvalues. However, the estimation of shrinkage parameter is neglected for semiparametric regression models. The m...