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Mixture Markov regression model with application to mosquito surveillance data analysis
Author(s) -
Gao Xin,
Cao Yurong R.,
Ogden Nicholas,
Aubin Louise,
Zhu Huaiping P.
Publication year - 2017
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201600137
Subject(s) - series (stratigraphy) , markov chain , mixture model , regression analysis , markov model , statistics , time series , expectation–maximization algorithm , computer science , mathematics , maximum likelihood , econometrics , paleontology , biology
A mixture Markov regression model is proposed to analyze heterogeneous time series data. Mixture quasi‐likelihood is formulated to model time series with mixture components and exogenous variables. The parameters are estimated by quasi‐likelihood estimating equations. A modified EM algorithm is developed for the mixture time series model. The model and proposed algorithm are tested on simulated data and applied to mosquito surveillance data in Peel Region, Canada.