An empirical hierarchical Bayes approach was first proposed to analyze gene expression data collected from microarray experiments. Through a closed-form variance shrinkage estimator, information from multiple genes is pooled to increase the statistical power of multiple hypothesis testing. The approach also allows various types of subject matter knowledge to be incorporated conveniently. Caveat...

In this paper, we examine the problem of estimating the sensitive characteristics and behaviors in a multinomial randomized response model using Bayesian approach. We derived a posterior distribution for parameter of interest for multinomial randomized response model. Based on the posterior distribution, we also calculated a credible intervals and mean squared error (MSE). We finally compare th...

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...

The possibility of employing explicitly defined functions of the observations as estimators of parametric functions in nonlinear regression analysis is explored. A general theory of best average mean square error estimation leading to explicit estimators is set forth. Such estimators are given a Bayesian interpretation as Fourier expansions of the estimator which minimizes expected posterior sq...

We consider finite-horizon fitted Q-iteration with linear function approximation to learn a policy from a training set of trajectories. We show that fitted Q-iteration can give biased estimates and invalid confidence intervals for the parameters that feature in the policy. We propose a regularized estimator called soft-threshold estimator, derive it as an approximate empirical Bayes estimator, ...

We propose an adaptive shrinkage estimator for use in regression problems charaterized by many predictors, such as wavelet estimation. Adaptive estimators perform well over a variety of circumstances, such as regression models in which few, some or many coefficients are zero. Our estimator, PolyShrink, adaptively varies the amount of shrinkage to suit the estimation task. Whereas hard threshold...

Robbins’s visionary 1951 paper can be seen as an exercise in binary classification, but also as a precursor to the outpouring of recent work on high-dimensional data analysis and multiple testing. It can also be seen as the birth of empirical Bayes methods. Our objective in the present note is to use this problem and several variants of it to provide a glimpse into the evolution of empirical Ba...

MOTIVATION There often are many alternative models of a biochemical system. Distinguishing models and finding the most suitable ones is an important challenge in Systems Biology, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Bayes factors are employed as a measure of evidential preference for one model over another....

We propose a class of estimators of the Bayes factor which is based on an extension of the bridge sampling identity of Meng & Wong (1996) and makes use of the output of the reversible jump algorithm of Green (1995). Within this class we give the optimal estimator and also a suboptimal one which may be simply computed on the basis of the acceptance probabilities used within the reversible jump a...

An application of Bayes theorem to seismic signal ltering is implemented. A best basis strategy is derived as an hypothesis test with maximum entropy priors. Cost functionals are derived. In this approach the best basis is determined as the basis least likely to t the prior noise model. Sub-band varianace estimates contain all of the information regarding the background noise. A Bayes estimator...