SIMULATION STUDIES OF THE KOLMOGOROV-SMIRNOV TEST ON DISTRIBUTIONS WITH UNKNOWN PARAMETERS.

Abstract

The standard Kolmogorov-Smirnov (K-S) test is valid when the null distribution is completely specified. Lilliefors (1967) shows for the case when the null distribution is normal, but the mean and variance are unknown and must be estimated from the data, that the K-S test is extremely conservative. This research constructs by Monte Carlo simulation tables of critical values for the K-S statistic when the null distribution is either negative exponential or rectangular with unknown parameters that are to be estimated from the data. The results indicate also that the standard K-S test is conservative. Moreover, power studies are simulated in both cases. These studies indicate the danger of using the K-S test for small samples. (Author)

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1968
Accession Number
AD0674236

Entities

People

  • F. M. Speed
  • W. B. Smith

Organizations

  • Texas A&M University

Tags

DTIC Thesaurus Topics

  • Computing-Related Activities
  • Data Science
  • Information Science
  • Interdisciplinary Science
  • Mathematical Analysis
  • Mathematics
  • Monte Carlo Method
  • Simulations
  • Standards

Fields of Study

  • Mathematics

Readers

  • Statistical inference.