Empirical Bayes Estimation With Kernel Sequence Method

Abstract

In this paper, we consider the empirical Bayes estimation in the exponential family. A minimax lower bound is derived. It is shown that the best possible rate of empirical Bayes estimators is O(1/n) if Theta is bounded. Then we turn to find an empirical Bayes estimator with a rate close to this lower bound rate. Applying the kernel sequence method, we are able to construct an empirical Bayes estimator with a rate of O(1/n(1n n)7(1n 1n n)2). Under the same assumption, this rate is the fastest compared to the earlier results published in the literature.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2001
Accession Number
ADA395790

Entities

People

  • Jinjun Lu
  • Shanti Gupta

Organizations

  • Purdue University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Classification
  • Construction
  • Convergence
  • Estimators
  • Information Operations
  • Kernel Functions
  • Literature
  • Mathematics
  • Military Research
  • Random Variables
  • Security
  • Sequences
  • Statistics
  • Universities

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation