Monte Carlo Techniques for Estimating Power in Aircraft T&E Tests

Abstract

Edwards AFB, as a matter of policy, requires statistical rigor be a part of test design and analysis. Statistically defensible methods are used to gain as much information as possible from each test. This requires: Statistically defensible methods be identified and applied to each test Setting up tests to maximize scope of inference, and Determining the power or each test to optimize sample size. This paper demonstrates how Monte Carlo techniques may be applied to aircraft test and evaluation to determine the power of the test and the associated sample size requirements. Traditional methods for determining the power of a test are based on distributional assumptions associated with data. These assumptions may not be appropriate; a distribution-free Monte Carlo technique for power assessment for tests with (possible) serially correlated data is presented. The technique is illustrated with an example from a target location error (TLE) test. Power of the test and appropriate sample sizes are derived using Monte Carlo simulation implemented in R.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2011
Accession Number
ADA545255

Entities

People

  • Todd Remund
  • William Kitto

Organizations

  • Air Force Test Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Aircrafts
  • Computing-Related Activities
  • Data Science
  • Department Of Defense
  • Information Operations
  • Information Science
  • Interdisciplinary Science
  • Mathematical Analysis
  • Monte Carlo Method
  • Simulations
  • Statistical Analysis
  • Statistics
  • Test And Evaluation
  • Uncertainty
  • United States

Fields of Study

  • Education

Readers

  • Aerospace Test and Evaluation
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference