This paper considers the estimation of parameters in a dynamic stochastic model for securities prices, where the expected rate of return is a random variable. An empirical Bayes estimator is developed from the model structure. The estimator is an improvement on other popular estimators in terms of mean squared error. The e ect of reduced estimation error on accumulated wealth is analyzed for th...

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...

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, ...

Embeddings of the CAR (canonical anticommutation relations) algebra of fermions into the Cuntz algebra O2 (or O2d more generally) are presented by using recursive constructions. As a typical example, an embedding of CAR onto the U(1)-invariant subalgebra of O2 is constructed explicitly. Generalizing this construction to the case of O2p , an embedding of CAR onto the U(1)-invariant subalgebra of...

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...

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....

In this paper, the generalized Bayes estimator of mean vector parameter for multivariate normal distribution with Unknown and covariance matrix is considered. This estimation performed under balanced-LINEX error loss function. The by using wavelet transformation investigated. We also prove admissibility minimaxity shrinkage we present simulation study real data set test validity new estimator.