Estimation of Kullback-Leibler information is a crucial part of deriving a statistical model selection procedure which, like AIC, is based on the likelihood principle. To discriminate between nested models, we have to estimate KullbackLeibler information up to the order of a constant, while Kullback-Leibler information itself is of the order of the number of observations. A correction term empl...

Recent developments in microarray technology make it possible to capture the gene expression profiles for thousands of genes at once. With this data researchers are tackling problems ranging from the identification of 'cancer genes' to the formidable task of adding functional annotations to our rapidly growing gene databases. Specific research questions suggest patterns of gene expression that ...

Under model correctness, highly accurate inference on a scalar interest parameter in the presence of a nuisance parameter can be achieved by several routes, among them considering the bootstrap distribution of the signed root likelihood ratio statistic. The context of model mis-specification is considered and inference based on a robust form of the signed root statistic is discussed in detail. ...

Copulas are used to model multivariate data as they account for the dependence structure and provide a flexible representation of the multivariate distribution. A great number of copulas has been proposed with various dependence aspects. One important issue is the choice of an appropriate copula from a large set of candidate families to model the data at hand. A large number of copulas are comp...

Uncertainty of insurance liabilities has always been the key issue in actuarial theory and practice. This is represented for instance by study and modeling of mortality in life insurance, and loss distributions in traditional actuarial science. These models have evolved from early simple deterministic calculations to more sophisticated probabilistic ones. Such probabilistic models have been tra...

Parametric mixture models are commonly used in applied work, especially empirical economics, where these models are commonly used to learn for example about the proportions of various types in a given population. It is well known that likelihood inference in these mixture models is complicated due to 1) lack of point identification, and 2) parameters (like some proportions) whose true value lie...

We introduce a nonparametric bootstrap approach for Quasi-Likelihood Ratio type tests of nonlinear restrictions. Our method applies to extremum estimators, such as quasimaximum likelihood and generalized method of moments estimators. Unlike existing parametric bootstrap procedures for Quasi-Likelihood Ratio type tests, our procedure constructs bootstrap samples in a fully nonparametric way. We ...