PROBLEMS OF STATISTICAL INFERENCE FOR BIRTH AND DEATH QUEUEING MODELS

Abstract

A large sample theory is presented for birth and death queueing processes which are ergodic and metrically transitive. The theory is applied to make inferences about how arrival and service rates vary with the number in the system. Likelihood ratio tests and maximum likelihood estimators are derived for simple models which describe this variation. Composite hypotheses such as that the arrival rate does not vary with the number in the system are considered. A numerical example illustrating these results is presented which includes the testing of both true and false composite hypotheses.

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Document Details

Document Type
Technical Report
Publication Date
Mar 25, 1963
Accession Number
AD0412934

Entities

People

  • R. W. Wolff

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Channel Models
  • Data Analysis
  • Data Science
  • Estimators
  • Government Procurement
  • Information Science
  • Intervals
  • Markov Processes
  • Operations Research
  • Probability
  • Probability Distributions
  • Queueing Theory
  • Random Variables
  • Statistical Inference
  • Theorems
  • Transitions
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms