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An empirical Bayes approach to statistics
 Proc Third Berkeley Symp Math Statist Probab
, 1956
"... Let X be a random variable which for simplicity we shall assume to have discrete values x and which has a probability distribution depending in a known way on an unknown real parameter A, (1) p (xIX) =Pr [X = xIA =X], Aitself being a random variable with a priori distribution function (2) G (X) =P ..."
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Cited by 138 (0 self)
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Let X be a random variable which for simplicity we shall assume to have discrete values x and which has a probability distribution depending in a known way on an unknown real parameter A, (1) p (xIX) =Pr [X = xIA =X], Aitself being a random variable with a priori distribution function (2) G (X) =Pr [A< X. The unconditional probability distribution of X is then given by
AN EMPIRICAL BAYES APPROACH TO ESTIMATING THE PROBABILITY OF CORRECT SELECTION
, 1984
"... rank:fng and select.ion; shr.inkage In the problem of selecting the best of k populations, Olkin, Sobel, and Tong (1976) have introduced the idea of estimating the probability of correct selection. In an attempt to improve on their estimator we consider an empirical Bayes approach. We compare the t ..."
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rank:fng and select.ion; shr.inkage In the problem of selecting the best of k populations, Olkin, Sobel, and Tong (1976) have introduced the idea of estimating the probability of correct selection. In an attempt to improve on their estimator we consider an empirical Bayes approach. We com
Empirical Bayes approach to block wavelet function estimation
, 2002
"... Wavelet methods have demonstrated considerable success in function estimation through termbyterm thresholding of empirical wavelet coecients. However, it has been shown that grouping the empirical wavelet coecients into blocks and making simultaneous threshold decisions about all coecients in e ..."
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Cited by 25 (1 self)
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in each block has a number of advantages over termbyterm thresholding, including asymptotic optimality and better mean squared error performance in nite sample situations. In this paper, we consider an empirical Bayes approach to incorporating information on neighbouring empirical wavelet coecients
An Empirical Bayes Approach to Inferring LargeScale Gene Association Networks
 BIOINFORMATICS
, 2004
"... Motivation: Genetic networks are often described statistically by graphical models (e.g. Bayesian networks). However, inferring the network structure offers a serious challenge in microarray analysis where the sample size is small compared to the number of considered genes. This renders many standar ..."
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Cited by 234 (6 self)
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, and (iii) a heuristic network search based on false discovery rate multiple testing. Steps (ii) and (iii) correspond to an empirical Bayes estimate of the network topology. Results: Using computer simulations we investigate the sensitivity (power) and specificity (true negative rate) of the proposed
Empirical Bayes approaches to mixture problems and wavelet regression
, 1998
"... We consider model selection in a hierarchical Bayes formulation of the sparse normal linear model in which individual variables have, independently, an unknown prior probability of being included in the model. The focus is on orthogonal designs, which are of particular importance in nonparametric ..."
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Cited by 23 (2 self)
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in nonparametric regression via wavelet shrinkage. Empirical Bayes estimates of hyperparameters are easily obtained via the EM algorithm, and this approach is contrasted with a recent conditional likelihood proposal. Our model selection approach yields a straightforward method for data dependent threshold
An empirical bayes approach to contextual region classification
 In CVPR
, 2009
"... This paper presents a nonparametric approach to labeling of local image regions that is inspired by recent developments in informationtheoretic denoising. The chief novelty of this approach rests in its ability to derive an unsupervised contextual prior over image classes from unlabeled test data. ..."
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Cited by 18 (0 self)
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the empirical Bayes technique of statistical inversion to recover a contextual model directly from the test data, either as a spatially varying or as a globally constant prior distribution over the classes in the image. Results on two challenging datasets convincingly demonstrate that useful contextual
An Empirical Bayes Approach to Estimating Loss Ratios
"... The empirical Bayes model is explored as a technique for estimating key financial variables that are meaningful to regulators, policyholders, and security analysts of propertyliability insurers. The time series mean and two empirical Bayes models of the loss ratio are evaluated for four lines of bu ..."
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The empirical Bayes model is explored as a technique for estimating key financial variables that are meaningful to regulators, policyholders, and security analysts of propertyliability insurers. The time series mean and two empirical Bayes models of the loss ratio are evaluated for four lines
On the empirical Bayes approach to adaptive filtering in the Gaussian model
"... this paper we will be dealing exclusively with the quadratic risk function #, #) = E #(#)) . Here by E we mean the conditional expectation given #. From now on we suppress the dependence of the expectation and some other quantities on #. There are basically two approaches most often use ..."
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used to study this problem: the classical Bayes approach often invoked within the framework of stationary random processes, and, more recently, the minimax approach which gained popularity after its use in Pinsker (1980). In fact, Pinsker (1980) combined both approaches, and it is this interplay
An Empirical Bayes Approach to Due Date Assignment
"... We consider a model in which there are m products and for each i=1,...,m, there are n i observations of the service times associated with the i product. Assuming the service times associated with the i product follow an exponential distribution with parameter i # , and i # 's are independ ..."
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;s are independent random variables with a common gamma distribution, with unknown parameters # and # , we provide two empirical Bayes, under the squared error loss, forecasts of the (n i +1) service time for each i. Our proposed empirical Bayes forecasts are obtained by replacing the structural parameters
Results 1  10
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1,366,218