Semiparametric estimation with generated covariates

Ridge Stochastic Restricted Estimators in Semiparametric Linear Measurement Error Models

In this article we consider the stochastic restricted ridge estimation in semipara-metric linear models when the covariates are measured with additive errors. The development of penalized corrected likelihood method in such model is the basis for derivation of ridge estimates. The asymptotic normality of the resulting estimates are established. Also, necessary and sufficient condition...

Semiparametric Bootstrap Prediction Intervals in time Series

One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by an autoregressive process. The...

Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing Covariates

Statistical Inference for Semiparametric Varying-coefficient Partially Linear Models with Error-prone Linear Covariates

We study semiparametric varying-coefficient partially linear models when some linear covariates are not observed, but ancillary variables are available. Semiparametric profile least-square based estimation procedures are developed for parametric and nonparametric components after we calibrate the error-prone covariates. Asymptotic properties of the proposed estimators are established. We also p...

Maximum Likelihood and Semiparametric Estimation in Logistic Models with Incomplete Covariate Data

Maximum likelihood estimation of regression parameters with incomplete covariate information usually requires a distributional assumption about the concerned covariates which implies a source of misspeciication. Semiparametric procedures avoid such assumptions at the expense of eeciency. A simulation study is carried out to get an idea of the performance of the maximum likelihood estima-tor und...