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
Aug 16, 2011
Accession Number
ADA548340

Entities

People

  • Todd Remund
  • William Kitto

Organizations

  • Air Force Test Center

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Air Force
  • Aircrafts
  • Confidence Limits
  • Data Analysis
  • Data Mining
  • Data Science
  • Information Science
  • Monte Carlo Method
  • Probability
  • Simulations
  • Standards
  • Statistical Analysis
  • Statistical Sampling
  • Statistical Tests
  • Statistics
  • Test And Evaluation

Fields of Study

  • Education

Readers

  • Aerospace Test and Evaluation
  • Computational Modeling and Simulation
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference