Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90945/1/observed_information_semi-parametric_models.pdf6512512
AbstractEfficiencies of the maximum pseudolikelihood estimator and a number of related estimators fo...
Classic Estimating Equations (CEE) were first introduced by Godambe and have been widely used under ...
Parametric regression models are widely used in public health sciences. This dissertation is concern...
Hjort & Claeskens (2003) developed an asymptotic theory for model selection, model averaging and sub...
We establish a general asymptotic theory for nonparametric maximum likelihood estimation in semipara...
AbstractWe consider likelihood based inference in a class of logistic models for case- control studi...
Maximum likelihood estimation is a standard approach when confronted with the task of finding estima...
Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational stu...
This paper develops the asymptotic theory for the estimation of smooth semiparametric generalized es...
Indirect inference estimators (i.e., simulation-based minimum distance estimators) in a parametric m...
AbstractWe propose an empirical likelihood-based estimation method for conditional estimating equati...
This paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smo...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90947/1/semiparametric_likelihood_ratio...
Abstract. We consider semiparametric regression problems for which the response function is known up...
AbstractEfficiencies of the maximum pseudolikelihood estimator and a number of related estimators fo...
Classic Estimating Equations (CEE) were first introduced by Godambe and have been widely used under ...
Parametric regression models are widely used in public health sciences. This dissertation is concern...
Hjort & Claeskens (2003) developed an asymptotic theory for model selection, model averaging and sub...
We establish a general asymptotic theory for nonparametric maximum likelihood estimation in semipara...
AbstractWe consider likelihood based inference in a class of logistic models for case- control studi...
Maximum likelihood estimation is a standard approach when confronted with the task of finding estima...
Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational stu...
This paper develops the asymptotic theory for the estimation of smooth semiparametric generalized es...
Indirect inference estimators (i.e., simulation-based minimum distance estimators) in a parametric m...
AbstractWe propose an empirical likelihood-based estimation method for conditional estimating equati...
This paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smo...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90947/1/semiparametric_likelihood_ratio...
Abstract. We consider semiparametric regression problems for which the response function is known up...
AbstractEfficiencies of the maximum pseudolikelihood estimator and a number of related estimators fo...
Classic Estimating Equations (CEE) were first introduced by Godambe and have been widely used under ...
Parametric regression models are widely used in public health sciences. This dissertation is concern...
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