Approximate Prediction Intervals for a Future Observation from the Inverse Gaussian Distribution.

Abstract

The problem of predicting, on the basis of an observed sample from an inverse Gaussian distribution, the mean of a future random sample (or a single future observation) from the same distribution is considered. Approximate prediction intervals are proposed, and their accuracy is investigated via extensive Monte Carlo simulations. The results are useful for predicting the next first passage time for a Brownian motion with positive drift or the failure time of an item having inverse Gaussian life distribution. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1980
Accession Number
ADA089927

Entities

People

  • William J. Padgett

Organizations

  • University of South Carolina

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Brownian Motion
  • Data Science
  • Distribution Functions
  • Distribution Theory
  • Gaussian Distributions
  • Information Science
  • Monte Carlo Method
  • Normal Distribution
  • Probability
  • Probability Density Functions
  • Random Variables
  • Reliability
  • Statistical Algorithms
  • Statistical Samples
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Computational Modeling and Simulation
  • Regression Analysis.