Design and Estimation in Small Sample Quantal Response Problems: A Monte Carlo Study

Abstract

In sensitivity testing for the Department of Defense, the high cost of experimental units necessitates the use of small sample sizes and accentuates the importance of design. This paper compares five data collection--estimation procedures. Four of these are modifications of the Robbins-Monro method, and the other is the Langlie. The simulation study is designed as a factorial experiment with response function, sample size, initial design point, gate width, and noise as factors. The estimated V sub 50 and its root mean square error are the responses compared to assess the small sample behavior of each method. Although there is no single clear-cut winner, the Delayed Robbins-Monro with maximum likelihood estimation and the Estimated Quantal Response Curve (Wu 1985) are shown to perform well over a broad variety of conditions.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1989
Accession Number
ADA208115

Entities

People

  • Barry A. Bodt
  • Henry B. Tingey

Organizations

  • Ballistic Research Laboratory

Tags

Communities of Interest

  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Combinatorial Analysis
  • Data Science
  • Department Of Defense
  • Distribution Functions
  • Estimators
  • Explosives
  • Information Science
  • Maximum Likelihood Estimation
  • Monte Carlo Method
  • Normal Distribution
  • Ordnance Laboratories
  • Sensitivity
  • Simulations
  • Statistical Algorithms
  • Statistics
  • Test Methods

Readers

  • Regression Analysis.
  • Theoretical Analysis.