Classical and Bayes-P Subset Selection Procedures for Double Exponential Populations

Abstract

The exact distribution of the sample mean from a double EXPONENTIAL(Laplace) model is derived. A classical subset selection procedure based on the sample mean for selecting the population associated with the largest location parameter of k double exponential(Laplace) distributions is studied. For the case when a non-informative prior is introduced into the problem, the relation between the classical Maximum-Type Procedure Rule Rmax and the so-called Bayes-P* subset selection procedure rule is studied. An improved bound for the guarantee probability of a correct selection for the classical subset selection rule Rmax that relates the rule Rmax to the selected subset size (notice that the subset selection rule Rmax may select all the populations) is studied and some improved rules of the type Rmax are provided. Exponential functions; Laplace transformations; Bayes theorem.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1990
Accession Number
ADA223830

Entities

People

  • Shanti Gupta
  • Yuning Liao

Organizations

  • Purdue University

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  • Materials and Manufacturing Processes

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  • Data Science
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  • Distribution Functions
  • Information Science
  • Military Research
  • Normal Distribution
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  • Probability Density Functions
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  • Mathematics

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  • Astronomy/Astrophysics
  • Regression Analysis.