Literature DB >> 26742002

Regime-Switching Bivariate Dual Change Score Model.

Sy-Miin Chow1, Kevin J Grimm2, Guillaume Filteau3, Conor V Dolan4, John J McArdle5.   

Abstract

Mixture structural equation model with regime switching (MSEM-RS) provides one possible way of representing over-time heterogeneities in dynamic processes by allowing a system to manifest qualitatively or quantitatively distinct change processes conditional on the latent "regime" the system is in at a particular time point. Unlike standard mixture structural equation models such as growth mixture models, MSEM-RS allows individuals to transition between latent classes over time. This class of models, often referred to as regime-switching models in the time series and econometric applications, can be specified as regime-switching mixture structural equation models when the number of repeated measures involved is not large. We illustrate the empirical utility of such models using one special case-a regime-switching bivariate dual change score model in which two growth processes are allowed to manifest regime-dependent coupling relations with one another. The proposed model is illustrated using a set of longitudinal reading and arithmetic performance data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 study (ECLS-K; U.S. Department of Education, National Center for Education Statistics, 2010).

Entities:  

Year:  2013        PMID: 26742002     DOI: 10.1080/00273171.2013.787870

Source DB:  PubMed          Journal:  Multivariate Behav Res        ISSN: 0027-3171            Impact factor:   5.923


  10 in total

1.  Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models and Individual Probability Plots.

Authors:  Michael C Neale; Shaunna L Clark; Conor V Dolan; Michael D Hunter
Journal:  Struct Equ Modeling       Date:  2015-06-26       Impact factor: 6.125

2.  Bayesian hidden Markov models for delineating the pathology of Alzheimer's disease.

Authors:  Kai Kang; Jingheng Cai; Xinyuan Song; Hongtu Zhu
Journal:  Stat Methods Med Res       Date:  2017-12-26       Impact factor: 3.021

3.  OpenMx 2.0: Extended Structural Equation and Statistical Modeling.

Authors:  Michael C Neale; Michael D Hunter; Joshua N Pritikin; Mahsa Zahery; Timothy R Brick; Robert M Kirkpatrick; Ryne Estabrook; Timothy C Bates; Hermine H Maes; Steven M Boker
Journal:  Psychometrika       Date:  2015-01-27       Impact factor: 2.500

4.  What's for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R.

Authors:  Lu Ou; Michael D Hunter; Sy-Miin Chow
Journal:  R J       Date:  2019-06       Impact factor: 3.984

5.  Zero-Inflated Regime-Switching Stochastic Differential Equation Models for Highly Unbalanced Multivariate, Multi-Subject Time-Series Data.

Authors:  Zhao-Hua Lu; Sy-Miin Chow; Nilam Ram; Pamela M Cole
Journal:  Psychometrika       Date:  2019-03-11       Impact factor: 2.500

6.  Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation-Maximization (SAEM) Algorithm.

Authors:  Sy-Miin Chow; Zhaohua Lu; Andrew Sherwood; Hongtu Zhu
Journal:  Psychometrika       Date:  2014-11-22       Impact factor: 2.500

7.  Bayesian adaptive group lasso with semiparametric hidden Markov models.

Authors:  Kai Kang; Xinyuan Song; X Joan Hu; Hongtu Zhu
Journal:  Stat Med       Date:  2018-11-28       Impact factor: 2.373

8.  Hidden Markov latent variable models with multivariate longitudinal data.

Authors:  Xinyuan Song; Yemao Xia; Hongtu Zhu
Journal:  Biometrics       Date:  2016-05-05       Impact factor: 2.571

Review 9.  Developmental cognitive neuroscience using latent change score models: A tutorial and applications.

Authors:  Rogier A Kievit; Andreas M Brandmaier; Gabriel Ziegler; Anne-Laura van Harmelen; Susanne M M de Mooij; Michael Moutoussis; Ian M Goodyer; Ed Bullmore; Peter B Jones; Peter Fonagy; Ulman Lindenberger; Raymond J Dolan
Journal:  Dev Cogn Neurosci       Date:  2017-11-22       Impact factor: 5.811

10.  Current methods and limitations for longitudinal fMRI analysis across development.

Authors:  Tara Madhyastha; Matthew Peverill; Natalie Koh; Connor McCabe; John Flournoy; Kate Mills; Kevin King; Jennifer Pfeifer; Katie A McLaughlin
Journal:  Dev Cogn Neurosci       Date:  2017-11-22       Impact factor: 6.464

  10 in total

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