Penalized empirical likelihood estimation of semiparametric models

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...

Penalized semiparametric density estimation

In this article we propose a penalized likelihood approach for the semiparametric density model with parametric and nonparametric components. An efficient iterative procedure is proposed for estimation. Approximate generalized maximum likelihood criterion from Bayesian point of view is derived for selecting the smoothing parameter. The finite sample performance of the proposed estimation approa...

Penalized Likelihood Estimation in Gaussian Mixture Models

Penalized Likelihood Density Estimation :

Semiparametric inference for transformation models via empirical likelihood

AMS 2000 subject classifications: 62N02 62G20 Keywords: Kaplan–Meier estimator Martingale Proportional hazards model Proportional odds model Right censoring U-statistic a b s t r a c t Recent advances in the transformation model have made it possible to use this model for analyzing a variety of censored survival data. For inference on the regression parameters, there are semiparametric procedur...