Some regularization methods, including the group lasso and the adaptive group lasso, have been developed for the automatic selection of grouped variables (factors) in conditional mean regression. In many practical situations, such a problem arises naturally when a set of dummy variables is used...

In the Bayesian stochastic search variable selection framework, a common prior distribution for the regression coefficients is the g-prior of Zellner. However there are two standard cases where the associated covariance matrix does not exist and the conventional prior of Zellner cannot be used:...

Here the problem of selecting the number of clusters in cluster analysis is considered. Recently, the concept of clustering stability, which measures the robustness of any given clustering algorithm, has been utilized in Wang (2010) for selecting the number of clusters through cross validation....

We study the problems of hypothesis testing and point estimation for the correlation coefficient between the disturbances in the system of two seemingly unrelated regression equations. An objective Bayesian solution to each problem is proposed based on combined use of the invariant loss function...

By a two-stage sampling procedure a confidence region for the largest and smallest means of several independent normal populations is constructed, where the population variances are unknown and possibly unequal. This confidence region can be completed after taking additional observations at the...

An adaptation of the Brier score and the concordance probability is proposed for the two-level and the three-level random intercept binary regression model. This results in 2 different Brier scores and 3 different C-indices for the two-level binary regression model and 4 different Brier scores...

In a sample of mRNA species counts, sequences without duplicates or with small numbers of copies are likely to carry information related to mutations or diseases and can be of great interest. However, in some situations, sequence abundance is unknown and sequencing the whole sample to find the...

Statistical analysis of max-stable processes used to model spatial extremes has been limited by the difficulty in calculating the joint likelihood function. This precludes all standard likelihood-based approaches, including Bayesian approaches. In this paper, we present a Bayesian approach...

To successfully translate genomic classifiers to the clinical practice, it is essential to obtain reliable and reproducible measurement of the classifier performance. A point estimate of the classifier performance has to be accompanied with a measure of its uncertainty. In general, this...

In breast cancer research, it is of great interest to identify genomic markers associated with prognosis. Multiple gene profiling studies have been conducted for such a purpose. Genomic markers identified from the analysis of single datasets often do not have satisfactory reproducibility. Among...