Estimating the transition matrix of a Markov chain observed at random times

Flavia Barsotti; Yohann De Castro; Thibault Espinasse; Paul Rochet

doi:10.1016/j.spl.2014.07.009

Estimating the transition matrix of a Markov chain observed at random times

Flavia Barsotti
, Yohann De Castro
, Thibault Espinasse
, Paul Rochet

UniCredit S.p.A
Université Paris-Saclay
Institut Camille Jordan
Laboratoire de Mathématiques Jean Leray

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

Résumé

We want to recover the transition kernel P of a Markov chain X when only a sub-sequence of X is available. The time gaps between the observations are iid with unknown distribution. We propose a method to build an estimator of P under the assumption that it has some zero entries. Its asymptotic performance is discussed in theory and through numerical simulations.

langue originale	Anglais
Pages (de - à)	98-105
Nombre de pages	8
journal	Statistics and Probability Letters
Volume	94
Les DOIs	https://doi.org/10.1016/j.spl.2014.07.009
état	Publié - 1 janv. 2014
Modification externe	Oui

Accès au document

10.1016/j.spl.2014.07.009

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title = "Estimating the transition matrix of a Markov chain observed at random times",

abstract = "We want to recover the transition kernel P of a Markov chain X when only a sub-sequence of X is available. The time gaps between the observations are iid with unknown distribution. We propose a method to build an estimator of P under the assumption that it has some zero entries. Its asymptotic performance is discussed in theory and through numerical simulations.",

keywords = "Asymptotic normality, Identifiability, Lie bracket, Parametric estimation, Sparse transition matrix, Time varying Markov process",

author = "Flavia Barsotti and \{De Castro\}, Yohann and Thibault Espinasse and Paul Rochet",

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AU - Barsotti, Flavia

AU - De Castro, Yohann

AU - Espinasse, Thibault

AU - Rochet, Paul

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Y1 - 2014/1/1

N2 - We want to recover the transition kernel P of a Markov chain X when only a sub-sequence of X is available. The time gaps between the observations are iid with unknown distribution. We propose a method to build an estimator of P under the assumption that it has some zero entries. Its asymptotic performance is discussed in theory and through numerical simulations.

AB - We want to recover the transition kernel P of a Markov chain X when only a sub-sequence of X is available. The time gaps between the observations are iid with unknown distribution. We propose a method to build an estimator of P under the assumption that it has some zero entries. Its asymptotic performance is discussed in theory and through numerical simulations.

KW - Asymptotic normality

KW - Identifiability

KW - Lie bracket

KW - Parametric estimation

KW - Sparse transition matrix

KW - Time varying Markov process

UR - https://www.scopus.com/pages/publications/84904910800

U2 - 10.1016/j.spl.2014.07.009

DO - 10.1016/j.spl.2014.07.009

M3 - Article

AN - SCOPUS:84904910800

SN - 0167-7152

VL - 94

SP - 98

EP - 105

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

ER -

Estimating the transition matrix of a Markov chain observed at random times

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