Fisher information and statistical inference for phase-type distributions

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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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
Publication date2011
Volume48A
Pages277-293
ISSN0021-9002
DOIs
StatePublished
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

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