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

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.

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Year of publication:	2013
Authors:	Zhou, Jie ; Han, Lu ; Liu, Sanyang
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 83.2013, 5, p. 1448-1456
Publisher:	Elsevier
Subject:	HIV dynamics \| Nonlinear state space model \| Mixed effect \| Sequential Monte Carlo \| EM algorithm

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010662333