We review the most common situations where one or some of the regularity conditions which underlie likelihood based parametric inference fail. Three main classes of problems will be treated: boundary problems, indeterminate parameters and singular information matrices, and change-point problems. The review is focused on the large- and small-sample properties of the likelihood ratio, though other approaches to hypothesis testing and connections to estimation will be mentioned in passing
The present paper discusses drawbacks and limitations of likelihood-based inference in sequential c...
Effective implementation of likelihood inference in models for high-dimensional data often requires ...
AbstractSuppose that independent observations come from an unspecified unknown distribution. Then we...
We review the most common situations where one or some of the regularity conditions which underlie l...
We review the most common situations where one or some of the regularity conditions which ...
This paper reviews the most common situations where one or more regularity conditions which underlie...
Parametric mixture models are commonly used in applied work, especially empirical economics, where t...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90947/1/semiparametric_likelihood_ratio...
AbstractLet x1,…, xn+1 be independent exponentially distributed random variables with intensity λ1 f...
We outline how modern likelihood theory, which provides essentially exact inferences in a variety of...
Maximum likelihood estimation is a standard approach when confronted with the task of finding estima...
We outline how modern likelihood theory, which provides essentially exact inferences in a variety of...
AbstractWe study the asymptotics of maximum-likelihood ratio-type statistics for testing a sequence ...
Plug-in estimation and corresponding refinements involving penalisation have been considered in vari...
This article develops a theory of maximum empirical likelihood estimation and empirical likelihood r...
The present paper discusses drawbacks and limitations of likelihood-based inference in sequential c...
Effective implementation of likelihood inference in models for high-dimensional data often requires ...
AbstractSuppose that independent observations come from an unspecified unknown distribution. Then we...
We review the most common situations where one or some of the regularity conditions which underlie l...
We review the most common situations where one or some of the regularity conditions which ...
This paper reviews the most common situations where one or more regularity conditions which underlie...
Parametric mixture models are commonly used in applied work, especially empirical economics, where t...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90947/1/semiparametric_likelihood_ratio...
AbstractLet x1,…, xn+1 be independent exponentially distributed random variables with intensity λ1 f...
We outline how modern likelihood theory, which provides essentially exact inferences in a variety of...
Maximum likelihood estimation is a standard approach when confronted with the task of finding estima...
We outline how modern likelihood theory, which provides essentially exact inferences in a variety of...
AbstractWe study the asymptotics of maximum-likelihood ratio-type statistics for testing a sequence ...
Plug-in estimation and corresponding refinements involving penalisation have been considered in vari...
This article develops a theory of maximum empirical likelihood estimation and empirical likelihood r...
The present paper discusses drawbacks and limitations of likelihood-based inference in sequential c...
Effective implementation of likelihood inference in models for high-dimensional data often requires ...
AbstractSuppose that independent observations come from an unspecified unknown distribution. Then we...