On Prediction Intervals for Future Observations from the Inverse Gaussian Distribution.

Abstract

The problem of predicting, on the basis of an observed sample of size n from an inverse Gaussian distribution, a future observation from the same distribution is discussed. Two prediction intervals that have been proposed in the literature, one of which is an approximate prediction interval, are compared using Monte Carlo simulations. The results indicate that in many of the simulated cases the approximate prediction interval is superior with respect to larger estimated coverage probabilities and smaller estimated mean lengths. This is true in particular for n at least 15 and for 95% and 99%.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1986
Accession Number
ADA170209

Entities

People

  • S. H. Tsoi
  • William J. Padgett

Organizations

  • University of South Carolina

Tags

DTIC Thesaurus Topics

  • Air Force
  • Data Science
  • Gaussian Distributions
  • Information Science
  • Monte Carlo Method
  • Observation
  • Probability
  • Quality Control
  • Reliability
  • Security
  • Simulations
  • South Carolina
  • Statistical Analysis
  • Statistical Samples
  • Statistics
  • Universities

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Statistical inference.