Penalized semiparametric density estimation

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...

Maximum penalized likelihood estimation in semiparametric capture-recapture models

We consider a semiparametric modeling approach for capture-recapture-recovery data where the temporal and/or individual variation of model parameters – usually the demographic parameters – is explained via covariates. Typically, in such analyses a fixed (or mixed) effects parametric model is specified for the relationship between the model parameters and the covariates of interest. In this pape...

Marginal longitudinal semiparametric regression via penalized splines.

We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achie...

Web-based Supplementary Materials for “A Penalized spline approach to functional mixed effects model analysis” by Huaihou Chen and Yuanjia Wang Semiparametric estimation of the within-subject vari- ation

Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models.

We propose a general strategy for variable selection in semiparametric regression models by penalizing appropriate estimating functions. Important applications include semiparametric linear regression with censored responses and semiparametric regression with missing predictors. Unlike the existing penalized maximum likelihood estimators, the proposed penalized estimating functions may not pert...