Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter.

Autor: Ou L; The Pennsylvania State University, State College, Pennsylvania, USA., Hunter MD; The Pennsylvania State University, State College, Pennsylvania, USA., Lu Z; The Pennsylvania State University, State College, Pennsylvania, USA., Stifter CA; The Pennsylvania State University, State College, Pennsylvania, USA., Chow SM; The Pennsylvania State University, State College, Pennsylvania, USA.
Jazyk: angličtina
Zdroj: The British journal of mathematical and statistical psychology [Br J Math Stat Psychol] 2023 Nov; Vol. 76 (3), pp. 462-490. Date of Electronic Publication: 2023 Sep 06.
DOI: 10.1111/bmsp.12318
Abstrakt: Many intensive longitudinal measurements are collected at irregularly spaced time intervals, and involve complex, possibly nonlinear and heterogeneous patterns of change. Effective modelling of such change processes requires continuous-time differential equation models that may be nonlinear and include mixed effects in the parameters. One approach of fitting such models is to define random effect variables as additional latent variables in a stochastic differential equation (SDE) model of choice, and use estimation algorithms designed for fitting SDE models, such as the continuous-discrete extended Kalman filter (CDEKF) approach implemented in the dynr R package, to estimate the random effect variables as latent variables. However, this approach's efficacy and identification constraints in handling mixed-effects SDE models have not been investigated. In the current study, we analytically inspect the identification constraints of using the CDEKF approach to fit nonlinear mixed-effects SDE models; extend a published model of emotions to a nonlinear mixed-effects SDE model as an example, and fit it to a set of irregularly spaced ecological momentary assessment data; and evaluate the feasibility of the proposed approach to fit the model through a Monte Carlo simulation study. Results show that the proposed approach produces reasonable parameter and standard error estimates when some identification constraint is met. We address the effects of sample size, process noise variance, and data spacing conditions on estimation results.
(© 2023 British Psychological Society.)
Databáze: MEDLINE
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