Nonparametric Selection, Ranking and Testing

Abstract

In this paper we consider the problem of ranking (partitioning) k populations according to the parameter which is defined as functionals of the distribution functions on the underlying populations. We obtain minimax rules for general loss functions, Bayes rules for some specific loss functions and purpose approximate non-randomized minimax rules. We also derive restricted minimax rules for selecting a subset of populations which are better than a control. Some nonparametric optimal test are derived for different hypotheses written in terms of the parameter as a functional of the underlying distribution function. Keywords: Selection and ranking; Nonparametric; Comparison with a control; Testing; Minimax decision rules; Mathematics.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1990
Accession Number
ADA219254

Entities

People

  • Sayaji N. Hande

Organizations

  • Purdue University

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Binomials
  • Clinical Trials
  • Distribution Functions
  • Military Research
  • Numbers
  • Probability
  • Random Variables
  • Real Numbers
  • Statistics
  • Universities

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Information Retrieval
  • AI & ML - Machine Learning Algorithms