Strathprints Home | Open Access | Browse | Search | User area | Copyright | Help | Library Home | SUPrimo

Confidence intervals for reliability growth models with small sample sizes

Quigley, J.L. and Walls, L.A. (2003) Confidence intervals for reliability growth models with small sample sizes. IEEE Transactions on Reliability, 52 (2). pp. 257-262. ISSN 0018-9529

[img]
Preview
PDF (CI for reliability growth models - March 2002) - Draft Version
Download (136Kb) | Preview

    Abstract

    Fully Bayesian approaches to analysis can be overly ambitious where there exist realistic limitations on the ability of experts to provide prior distributions for all relevant parameters. This research was motivated by situations where expert judgement exists to support the development of prior distributions describing the number of faults potentially inherent within a design but could not support useful descriptions of the rate at which they would be detected during a reliability-growth test. This paper develops inference properties for a reliability-growth model. The approach assumes a prior distribution for the ultimate number of faults that would be exposed if testing were to continue ad infinitum, but estimates the parameters of the intensity function empirically. A fixed-point iteration procedure to obtain the maximum likelihood estimate is investigated for bias and conditions of existence. The main purpose of this model is to support inference in situations where failure data are few. A procedure for providing statistical confidence intervals is investigated and shown to be suitable for small sample sizes. An application of these techniques is illustrated by an example.

    Item type: Article
    ID code: 4369
    Keywords: reliability, reliability management, management theory, statistics, Management. Industrial Management, Risk Management, Statistics, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality
    Subjects: Social Sciences > Industries. Land use. Labor > Management. Industrial Management
    Social Sciences > Industries. Land use. Labor > Risk Management
    Social Sciences > Statistics
    Department: Strathclyde Business School > Management Science
    Related URLs:
      Depositing user: Strathprints Administrator
      Date Deposited: 23 Oct 2007
      Last modified: 18 Jul 2014 01:27
      URI: http://strathprints.strath.ac.uk/id/eprint/4369

      Actions (login required)

      View Item

      Fulltext Downloads: