Adjustment for Missingness Using Auxiliary Information in Semiparametric Regression

Web - based Supplementary Materials for ( Adjustment for Missingness Using Auxiliary Information in Semiparametric Regression ) by ( Donglin Zeng and Qingxia Chen ) 1 Web

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...

Censored Quantile Regression with Auxiliary Information

In quantile regression of survival data, the estimation of the regression coefficients for extreme quantiles can be affected by severe censoring. Measurement error in covariates also leads to bias and loss in efficiency of estimators. In this seminar, we discuss the methodologies that effectively use the auxiliary information to improve the efficiency of censored quantile regression estimators....

Efficient quantile regression with auxiliary information

We discuss efficient estimation in quantile regression models where the quantile regression function is modeled parametrically. Additionally we assume that auxiliary information is available in the form of a conditional constraint. This is, for example, the case if the mean regression function or the variance function can be modeled parametrically, e.g. by a line or a polynomial. In this paper ...

Semiparametric Regression

Semiparametric regression is concerned with the flexible incorporation of nonlinear functional relationships in regression analyses. Any application area that uses regression analysis can benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a con...