Hidden Markov model likelihoods and their derivatives behave like I.I.D. ones

Peter J. Bickel, Ya'acov Ritov*, Tobias Rydén

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

We consider the log-likelihood function of hidden Markov models, its derivatives and expectations of these (such as different information functions). We give explicit expressions for these functions and bound them as the size of the chain increases. We apply our bounds to obtain partial second order asymptotics and some qualitative properties of a special model as well as to extend some results of [19].

Original languageEnglish
Pages (from-to)825-846
Number of pages22
JournalAnnales de l'institut Henri Poincare (B) Probability and Statistics
Volume38
Issue number6
DOIs
StatePublished - 2002

Keywords

  • Asymptotic normality
  • Hidden Markov model
  • Incomplete data
  • Missing data

Fingerprint

Dive into the research topics of 'Hidden Markov model likelihoods and their derivatives behave like I.I.D. ones'. Together they form a unique fingerprint.

Cite this