AbstractWe consider likelihood based inference in a class of logistic models for case- control studies with a partially observed covariate. The likelihood is a combination of a nonparametric mixture, a parametric likelihood, and an empirical likelihood. We prove the asymptotic normality of the maximum likelihood estimator for the regression slope, the asymptotic chi-squared distribution of the likelihood ratio statistic, and the consistency of the observed information, in both the prospective and the retrospective model
AbstractThis paper shows how the generalised empirical likelihood method can be used to obtain valid...
We establish a general asymptotic theory for nonparametric maximum likelihood estimation in semipara...
We consider large sample inference in a semiparametric logistic/proportional-hazards mixture model. ...
AbstractWe consider likelihood based inference in a class of logistic models for case- control studi...
We consider likelihood based inference in a class of logistic models for case-control studies with a...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90947/1/semiparametric_likelihood_ratio...
AbstractIn this paper, we consider the application of the empirical likelihood method to partially l...
Hjort & Claeskens (2003) developed an asymptotic theory for model selection, model averaging and sub...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90945/1/observed_information_semi-param...
AbstractWe introduce a notion of partial likelihood for binary statistical experiments, when the rel...
In this paper, we consider the log-likelihood ratio test (LRT) for testing the number of components ...
We consider estimation and confidence regions for the parameters[alpha]and[beta]based on the observa...
In this paper, we consider the application of the empirical likelihood method to partially linear mo...
Marginal likelihood and conditional likelihood are often used for eliminating nuisance parameters. F...
AbstractThis paper shows how the generalised empirical likelihood method can be used to obtain valid...
We establish a general asymptotic theory for nonparametric maximum likelihood estimation in semipara...
We consider large sample inference in a semiparametric logistic/proportional-hazards mixture model. ...
AbstractWe consider likelihood based inference in a class of logistic models for case- control studi...
We consider likelihood based inference in a class of logistic models for case-control studies with a...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90947/1/semiparametric_likelihood_ratio...
AbstractIn this paper, we consider the application of the empirical likelihood method to partially l...
Hjort & Claeskens (2003) developed an asymptotic theory for model selection, model averaging and sub...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90945/1/observed_information_semi-param...
AbstractWe introduce a notion of partial likelihood for binary statistical experiments, when the rel...
In this paper, we consider the log-likelihood ratio test (LRT) for testing the number of components ...
We consider estimation and confidence regions for the parameters[alpha]and[beta]based on the observa...
In this paper, we consider the application of the empirical likelihood method to partially linear mo...
Marginal likelihood and conditional likelihood are often used for eliminating nuisance parameters. F...
AbstractThis paper shows how the generalised empirical likelihood method can be used to obtain valid...
We establish a general asymptotic theory for nonparametric maximum likelihood estimation in semipara...
We consider large sample inference in a semiparametric logistic/proportional-hazards mixture model. ...