مقال لنمذجة سلاسل زمنية

In this paper, we focus on studying the Mixed-Effects State-Space

(MESS) models previously introduced by Liu et al. [Liu D, Lu T, Niu

X-F, et al. Mixed-effects state-space models for analysis of longitudinal

dynamic systems. Biometrics. 2011;67(2):476–485]. We propose

an estimation method by combining the auxiliary particle learning

and smoothing approach with the Expectation Maximization (EM)

algorithm. First, we describe the technical details of the algorithm

steps. Then, we evaluate their effectiveness and goodness of fit

through a simulation study. Our method requires expressing the posterior

distribution for the random effects using a sufficient statistic

that can be updated recursively, thus enabling its application to various

model formulations including non-Gaussian and nonlinear cases.

Finally, we demonstrate the usefulness of our method and its capability

to handle the missing data problem through an application to

a real dataset.