We develop a novel approach for testing treatment effects in high-throughput data. Most previous works on this topic focused on testing for differences between the means, but recently it has been recognized that testing for differential variation is probably as important. We take it a step...

Gibbs sampling has had great success in the analysis of mixture models. In particular, the “latent variable” formulation of the mixture model greatly reduces computational complexity. However, one failing of this approach is the possible existence of almost-absorbing states, called trapping...

type="main" xml:id="insr12052-abs-0001" <title type="main">Summary</title>In this article, we develop a modern perspective on Akaike's information criterion and Mallows's C_p for model selection, and propose generalisations to spherically and elliptically symmetric distributions. Despite the differences in their respective...

type="main" xml:id="ecin12133-abs-0001" <fi>Journal rankings published by Journal Citation Reports (JCR) are widely used to assess research quality, which influences important decisions by academic departments, universities, and countries in the allocation of research funds. We study refereed law...</fi>

We have developed Bayesian Markov chain Monte Carlo (MCMC) methods for inferences of continuous-time models with stochastic volatility and infinite-activity Lévy jumps using discretely sampled data. Simulation studies show that (i) our methods provide accurate joint identification of diffusion,...

It will be shown that the power-divergence family of goodness-of-fit statistics for completely specified parameters and nuisance parameter, under the sparseness assumption, have the same asymptotic normal distribution under a sequence of local alternatives. Hence, these tests have such low...

Let X,V1,...,Vn-1 be n random vectors in with joint density of the formwhere both [theta] and [Sigma] are unknown. We consider the problem of the estimation of [theta] with the invariant loss ([delta]-[theta])'[Sigma]-1([delta]-[theta]) and propose estimators which dominate the usual estimator...