Nonlinear mixed-effects state space models with applications to HIV dynamics

Jie Zhou, Lu Han and Sanyang Liu

Statistics & Probability Letters, 2013, vol. 83, issue 5, 1448-1456

Abstract: Nonlinear state space models with mixed-effect (NLMESSM) are proposed to model HIV clinical longitudinal data. With NLMESSM, filtering algorithms are proposed to estimate the individual/population states. Maximum likelihood via iterated filtering and variance components model are proposed to estimate fixed/random effects respectively. Simulation results validate the effectiveness of NLMESSM.

Keywords: HIV dynamics; Nonlinear state space model; Mixed effect; Sequential Monte Carlo; EM algorithm (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1016/j.spl.2013.01.032

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