A penalized likelihood estimation approach to semiparametric sample selection binary response modeling

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...

A Penalized Nonparametric Maximum Likelihood Approach to Species Richness Estimation

We propose a class of penalized nonparametric maximum likelihood estimators (NPMLEs) for the species richness problem. We use a penalty term on the likelihood because likelihood estimators that lack it have an extreme instability problem. The estimators are constructed using a conditional likelihood that is simpler than the full likelihood. We show that the full-likelihood NPMLE solution given ...

Penalized Likelihood Density Estimation :

Two - step estimation of binary response models with sample selection

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