Fisher information and statistical inference for phase-type distributions

Mogens Bladt, Luz Judith R Esparza, Bo Friis Nielsen

    Research output: Contribution to journalJournal articleResearchpeer-review


    This paper is concerned with statistical inference for both continuous and discrete phase-type distributions. We consider maximum likelihood estimation, where traditionally the expectation-maximization (EM) algorithm has been employed. Certain numerical aspects of this method are revised and we provide an alternative method for dealing with the E-step. We also compare the EM algorithm to a direct Newton--Raphson optimization of the likelihood function. As one of the main contributions of the paper, we provide formulae for calculating the Fisher information matrix both for the EM algorithm and Newton--Raphson approach. The inverse of the Fisher information matrix provides the variances and covariances of the estimated parameters.
    Original languageEnglish
    JournalJournal of Applied Probability
    Pages (from-to)277-293
    Publication statusPublished - 2011


    • Newton-Raphson
    • Phase-type distribution
    • Fisher information
    • EM algorithm


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