The choice of generalized linear mixed models is difficult, because it involves the selection of both fixed and random effects. Classical criteria like Akaike’s information criterion (AIC) are often not suitable for the latter task, and others which are useful in linear mixed models are...

Multivariate probit models have the appealing feature of capturing some of the dependence structure between the components of multidimensional binary responses. The key for the dependence modelling is the covariance matrix of an underlying latent multivariate Gaussian. Most approaches to maximum...

Variable selection is a venerable problem in multivariate statistics. Simulated annealing is one of a variety of metaheuristics that can be gainfully employed for variable selection; however, its effectiveness is influenced by algorithm design features such as the construction of the initial...

Testing the proportionality of two large-dimensional covariance matrices is studied. Based on modern random matrix theory, a pseudo-likelihood ratio statistic is proposed and its asymptotic normality is proved as the dimension and sample sizes tend to infinity proportionally.

A new method is proposed for the correction of confidence intervals when the original interval does not have the correct nominal coverage probabilities in the frequentist sense. The proposed method is general and does not require any distributional assumptions. It can be applied to both...

An ROC (Receiver Operating Characteristic) curve is a popular tool in the classification of two populations. The nonparametric additive model is used to construct a classifier which is estimated by maximizing the U-statistic type of empirical AUC (Area Under Curve). In particular, the sparsity...

Feature selection is an important step when building a classifier on high dimensional data. As the number of observations is small, the feature selection tends to be unstable. It is common that two feature subsets, obtained from different datasets but dealing with the same classification...

Given a target variable and observational data, we propose a sequential learning approach for discovering direct cause and effect variables of the target under the causal network framework. In the approach, we start from the target, sequentially find Markov blankets of variables and learn local...

To help settle the debate triggered the day after any election around the origin and destination of the vote of winners and losers, a Bayesian analysis of the results in a pair of consecutive elections is proposed. It is based on a model that simultaneously carries out a cluster analysis of the...

Sample size determination is essential to planning clinical trials. Jung (2008) established a sample size calculation formula for paired right-censored data based on the logrank test, which has been well-studied for comparing independent survival outcomes. An alternative to rank-based methods...