• DocumentCode
    778871
  • Title

    Solving ML equations for 2-parameter Poisson-process models for ungrouped software-failure data

  • Author

    Knafl, George J. ; Morgan, Joseph

  • Author_Institution
    DePaul Univ., Chicago, IL, USA
  • Volume
    45
  • Issue
    1
  • fYear
    1996
  • fDate
    3/1/1996 12:00:00 AM
  • Firstpage
    42
  • Lastpage
    53
  • Abstract
    Existence conditions are given for maximum likelihood (ML) parameter estimates for several families of 2-parameter software-reliability Poisson-process models. For each such model, the ML equations can be expressed in terms of one equation in one unknown. Bounds are given on solutions to these one equation problems to serve as initial intervals for search algorithms like bisection. Uniqueness of the solutions is established in some cases. Solutions are also tabulated for certain simple cases. Results are given for ungrouped failure data (exact times are available for all failures). ML estimation problems for such a situation are treated as limiting cases of problems based on failure times grouped into intervals of decreasing mesh
  • Keywords
    failure analysis; maximum likelihood estimation; parameter estimation; reliability theory; software reliability; stochastic processes; bisection; initial intervals; maximum likelihood parameter estimates; search algorithms; software reliability; solution uniqueness; two-parameter Poisson-process models; ungrouped failure data; ungrouped software failure data; Least squares approximation; Maximum likelihood estimation; Organizing; Parameter estimation; Poisson equations; Shape; Software reliability;
  • fLanguage
    English
  • Journal_Title
    Reliability, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9529
  • Type

    jour

  • DOI
    10.1109/24.488915
  • Filename
    488915