In a linear regression model with homoscedastic Normal noise, I consider James–Stein type shrinkage in the estimation of nuisance parameters associated with control variables. For at least three control variables and exogenous treatment, I show that the standard leastsquares estimator is dominated with respect to squared-error loss in the treatment effect even among unbiased estimators and even...

A new genus of troglobitic Carychiidae Jeffreys, 1830 is designated from Nodong Cave, North Chungcheong Province, Danyang, South Korea. This remarkable find represents a great range extension and thus, a highly distant distribution of troglobitic Carychiidae in Asia. The Zospeum-like, carychiid snails were recently included, without a formal description, in records documenting Korean malacofaun...

We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction strategy with Jeffreys prior and sequential normalized maximum likelihood (SNML) coincide and are optimal if and only if the latter is exchangeable, and if and only if the optimal strategy can be calculated without knowing the time horizon in advance. T...

Bayesian inference in factor analytic models has received renewed attention in recent years, partly due to computational advances but also partly to applied focuses generating factor structures as exemplified by recent work in financial time series modeling. The focus of our current work is to investigate the commonly overlooked problem of prior specification and sensitivity in factor models. W...

A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast literature on potential defaults including uniform priors, Jeffreys’ priors, reference priors, maximum entropy priors, and weakly informative priors. These methods, however, often manifest a key conceptual tension in prior modeling: a model encoding true prior information should be chosen without ...

The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. However, most of these methods have free parameters which have to be adjusted or estimated. In this paper, we propose a wavelet-based denoising technique without any free parameters; it is, in this sense, a "universal" method. Our approach uses empirical Bayes...

In an earlier study, Swain et al. reported on two statistical separability measures which for multiclass feature selection were shown experimentally to be more reliable than divergence. However, the empirical results of that study together with the best theoretical results in the literature left open some practical questions regarding the quantitative characterization of these separability meas...

In this study, we propose estimates for the confidence interval common mean of several zero-inflated gamma (ZIG) distributions based on fiducial generalized (GCI) and Bayesian highest posterior density (HPD) methods Jeffreys rule or uniform prior. Their performances in terms their coverage probabilities expected lengths are compared via a Monte Carlo simulation study. For almost all scenarios c...

In this paper, we treat estimation and prediction problems where negative multinomial variables are observed and, in particular, consider unbalanced settings. First, the problem of estimating multiple parameter vectors under standardized squared error loss is treated a new empirical Bayes estimator which dominates uniformly minimum variance unbiased (UMVU) suitable conditions derived. Next, joi...