APPROXIMATE GENERATION OF RANDOM VARIABLES.

Abstract

In many situations when a Monte Carlo procedure is used to solve some statistical problems, the computational work can be very excessive, since the number of samples, N, must usually be very large in order to obtain an adequate degree of accuracy. Let x sub 1, x sub 2,...,x sub n be a random sample drawn from the parental distribution f(x) with cumulative distribution F(x) and let h(x sub i) be a statistic which is a function of the observations x sub i. Then, the Monte Carlo procedure for evaluating P(h = or < H) may be described as follows: (a) generate random uniform variates, u sub i; (b) transform the uniform variate u sub i into x sub i with the help of x sub i = (F superscript -1)(u sub i); (c) evaluate the statistic h(x sub i) and determine the proportion for which h(x sub k) = or < H. The present paper is concerned with part (b) only.

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1968
Accession Number
AD0674240

Entities

People

  • S. Suharto

Organizations

  • Texas A&M University

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Data Science
  • Demographic Cohorts
  • Information Science
  • Mathematics
  • Observation
  • Random Variables
  • Statistical Samples

Fields of Study

  • Mathematics

Readers

  • Analytical Mechanics
  • Statistical inference.
  • Systems Analysis and Design