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
fDate :
3/1/1996 12:00:00 AM
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;
Journal_Title :
Reliability, IEEE Transactions on