title: Mixture Markov regression model with application to mosquito surveillance data analysis.
abstract: 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.
authors.affiliation: Department of Mathematics and Statistics, York University, Canada.
authors.forename: Xin
authors.initials: X
authors.lastname: Gao
authors.affiliation: Department of Mathematics and Statistics, York University, Canada.
authors.forename: Yurong R
authors.initials: YR
authors.lastname: Cao
authors.affiliation: National Microbiology Laboratory, Public Health Agency of Canada, Canada.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Ogden
authors.affiliation: Environmental Health, Public Health Services, Region of Peel, Canada.
authors.forename: Louise
authors.initials: L
authors.lastname: Aubin
authors.affiliation: Department of Mathematics and Statistics, York University, Canada.
authors.forename: Huaiping P
authors.initials: HP
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D000818:Animals; D001699:Biometry / Q000379:methods*; D002170:Canada; D009033:Culicidae / Q000502:physiology; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D011159:Population Surveillance; D011336:Probability; D012044:Regression Analysis
keywords: Clustering; Estimating equation; Markov model; Mixture model; Quasi-likelihood