System Availability: Time Dependence and Statistical Inference by (Semi) Non-Parametric Methods

Abstract

This paper addresses the assessment of simple system availability when there is concern about (a) time-dependence, so demands for system performance are not necessarily when the system is in steady-state,' as is often assumed, and when (b) information about system failures and repairs is in the form of observed data so questions of statistical influence arise. The methods and models involved lean towards the semi-parametric or non-parametric; in particular we employ the empirical Laplace transform in the time-dependent scenarios of interest. The authors propose analytically simple approximations to time-dependent system behavior, and assess the effects of model specification (up and down time dependence) upon rates of approach to a long-run steady state as the latter are estimated from available data (assumed to be a random sample).

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1988
Accession Number
ADA200579

Entities

People

  • Donald P. Gaver Jr.
  • Patrica A. Jacobs

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Availability
  • Computational Science
  • Confidence Limits
  • Data Science
  • Errors
  • Information Science
  • Markov Chains
  • Markov Processes
  • Operations Research
  • Probability
  • Random Variables
  • Simulations
  • Statistical Analysis
  • Statistical Inference
  • Steady State
  • Stochastic Processes
  • Time Dependence

Readers

  • Calculus or Mathematical Analysis
  • Logistics and Supply Chain Management.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference