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Open AccessJournal ArticleDOI

Rates of Convergence in Empirical Bayes Estimation Problems: Continuous Case

Pi-Erh Lin
- 01 Jan 1975 - 
- Vol. 3, Iss: 1, pp 155-164
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TLDR
In this article, a sequence of empirical Bayes estimators for a density function and its derivatives, which are not assumed to be uniformly bounded, using classes of kernel functions is proposed.
Abstract
In this paper we construct sequences of estimators for a density function and its derivatives, which are not assumed to be uniformly bounded, using classes of kernel functions. Utilizing these estimators, a sequence of empirical Bayes estimators is proposed. It is found that this sequence is asymptotically optimal in the sense of Robbins (Ann. Math. Statist. 35 (1964) 1-20). The rates of convergence of the Bayes risks associated with the proposed empirical Bayes estimators are obtained. It is noted that the exact rate is $n^{-q}$ with $q \leqq \frac{1}{3}$. An example is given and an explicit kernel function is indicated.

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Citations
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Journal ArticleDOI

Empirical Bayes Estimation in Lebesgue-Exponential Families with Rates Near the best Possible Rate

R. S. Singh
- 01 Jul 1979 - 
TL;DR: In this paper, an empirical Bayes (EB) estimator for the component problem is proposed and the best possible speed at which these estimators converge to the minimum EB risk is investigated.
Posted ContentDOI

Empirical Bayes with rates and best rates of convergence in u(x)C(θ) exp(-x/θ)-family: Estimation case

TL;DR: In this paper, a sequence of independent random vectors where Xi, conditional on % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9
Journal ArticleDOI

Empirical Bayes estimation in a multiple linear regression model

TL;DR: In this article, the authors considered the problem of estimating the vector β of the regression coefficients in a multiple linear regression with a completely unknown and unspecified distribution and the error vector e having a multivariate standard normal distribution.
Journal ArticleDOI

A Useful Empirical Bayes Identity

TL;DR: In this paper, the authors look at the underlying circumstance of when a simple empirical Bayes estimator is available, and show its occurrence not to be happenstance, but rather a special case of the problem of using past observations to estimate either the prior or the Bayes estimate.
Journal ArticleDOI

Estimation of prior distribution and empirical bayes estimation in a nonexponential family

TL;DR: Empirical Bayes squared error loss estimation for a nonexponential family with densities ⊆(x¦θ) = exp( - (x − θ))I(x > θ) for θ ϵ Θ, a subset of the real line, is considered.
References
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Book ChapterDOI

An Empirical Bayes Approach to Statistics

TL;DR: In this paper, a random variable with a priori distribution function is considered, and a probability distribution depending in a known way on an unknown real parameter A, where A is assumed to have discrete values.
Journal ArticleDOI

The Empirical Bayes Approach to Statistical Decision Problems

TL;DR: The empirical Bayes approach is applicable when the same decision problem presents itself repeatedly and independently with a fixed but unknown a priori distribution of the parameter as mentioned in this paper, which is the case of statistical decision problems in practice.
Book

Empirical Bayes methods

J.S. Maritz
TL;DR: In this article, empirical Bayes rules for a variety of special models are described and the problem of assessing the goodness of an empirical estimator for a given set of prior data is addressed.