One-Sided R-Reliable Intervals and Their Associated Confidences on the Sum of Two Continuous/Independent, Random Variables.

Abstract

The purpose of this proposal is to define and determine an R-reliable interval and its associated confidence on the sum of two continuous, independent, random variables with different scale parameters. The relationship between the confidence of the R-reliable interval on the sum and the confidences of the R-reliable intervals on the summand variables was also investigated. The confidence of the R-reliable interval on the sum was defined, and the largest order statistics were chosen which simplified the expression for the confidence of the R-reliable interval on the sum. The choice of the largest order statistics led to nonparametric results for the confidences of the R-reliable intervals on the summand variables. Exponential and folded normal continuous distributions were considered, and numerical values of the confidences associated with the R-reliable interval on the sum were obtained for selected sample sizes and selected ratios of the scale parameters. A distribution-free bound for the confidence of the R-reliable interval on the sum in terms of the confidence associated with the R-reliable intervals on the summand variables was obtained. The monotonicity of the confidence of the R-reliable interval on the sum was established for specific values of sample sizes. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1984
Accession Number
ADA143387

Entities

People

  • S. A. Patil

Organizations

  • Tennessee Technological University

Tags

Communities of Interest

  • Air Platforms
  • C4I

DTIC Thesaurus Topics

  • Data Science
  • Distribution Functions
  • Equations
  • Inequalities
  • Information Science
  • Mathematics
  • Normal Distribution
  • Order Statistics
  • Probability
  • Probability Density Functions
  • Random Variables
  • Security
  • Statistical Samples
  • Statistics
  • Surveys
  • Theorems

Readers

  • Regression Analysis.
  • Statistical inference.