Statistical practice often requires combining evidence from independent sources. A popular approach is to combine p-values. Motivated by the observation that p-values under the alternative are stochastically smaller than p-values under the null, we develop new combination rules that explicitly...

A new estimator for estimating the size of an elusive target population is presented using frequency counts from capture-recapture sampling. The proposed estimator is developed by extending the idea of Chao's estimator using monotonicity of ratios of neighbouring frequency counts under a...

The problem of missing data in building multidimensional composite indicators is a delicate problem which is often underrated. An imputation method particularly suitable for categorical data is proposed. This method is discussed in detail in the framework of nonlinear principal component...

For clustered survival data, the traditional Gehan-type estimator is asymptotically equivalent to using only the between-cluster ranks, and the within-cluster ranks are ignored. The contribution of this paper is two fold, (i) incorporating within-cluster ranks in censored data analysis, and (ii)...

The problem of variable selection within the class of generalized additive models, when there are many covariates to choose from but the number of predictors is still somewhat smaller than the number of observations, is considered. Two very simple but effective shrinkage methods and an extension...

The occurrence of negative variance components is a reasonably well understood phenomenon in the case of linear models for hierarchical data, such as variance-component models in designed experiments or linear mixed models for longitudinal data. In many cases, such negative variance components...

Generalized linear mixed models or latent variable models for categorical data are difficult to estimate if the random effects or latent variables vary at non-nested levels, such as persons and test items. Clayton and Rasbash (1999) suggested an Alternating Imputation Posterior (AIP)...

When two interventions are randomized to multiple sub-clusters within a whole cluster, accounting for the within sub-cluster (intra-cluster) and between sub-clusters (inter-cluster) correlations is needed to produce valid analyses of the effect of interventions. With the growing interest in...

Mixed effects models provide a suitable framework for statistical inference in a wide range of applications. The validity of likelihood inference for this class of models usually depends on the assumptions on random effects. We develop diagnostic tools for detecting random-effects model...