Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models

Xia, Ye-Mao; Tang, Niansheng; Gou, Jianwei

doi:10.1016/j.jmva.2016.09.001

Cited by 10 publications

(7 citation statements)

References 37 publications

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“…Also, it applies CFA, and thus already assumes a certain factor structure, and is thus too restrictive to detect many MM differences. A few methods exist that combine FA with LMM and thus could potentially be useful for identifying violations of MI over time 2 (Asparouhov, Hamaker, & Muthen, 2017;Song, Xia, & Zhu, 2017;Xia, Tang, & Gou, 2016). However, these methods also apply CFA, making them too restrictive to detect all kinds of MM differences.…”

Section: Introductionmentioning

confidence: 99%

Latent Markov Factor Analysis for Exploring Measurement Model Changes in Time-Intensive Longitudinal Studies

Vogelsmeier

Vermunt

Roekel

et al. 2019

Structural Equation Modeling: A Multidisciplinary Journal

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When time-intensive longitudinal data are used to study daily-life dynamics of psychological constructs (e.g., well-being) within persons over time (e.g., by means of experience sampling methodology), the measurement model (MM)-indicating which constructs are measured by which items-can be affected by time-or situation-specific artifacts (e.g., response styles and altered item interpretation). If not captured, these changes might lead to invalid inferences about the constructs. Existing methodology can only test for a priori hypotheses on MM changes, which are often absent or incomplete. Therefore, we present the exploratory method "latent Markov factor analysis" (LMFA), wherein a latent Markov chain captures MM changes by clustering observations per subject into a few states. Specifically, each state gathers validly comparable observations, and state-specific factor analyses reveal what the MMs look like. LMFA performs well in recovering parameters under a wide range of simulated conditions, and its empirical value is illustrated with an example.

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Section: Introductionmentioning

confidence: 99%

Latent Markov Factor Analysis for Exploring Measurement Model Changes in Time-Intensive Longitudinal Studies

Vogelsmeier

Vermunt

Roekel

et al. 2019

Structural Equation Modeling: A Multidisciplinary Journal

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“…For example, in the cocaine use data analysis, z it is often identified with the latent state of patient i at time t, then Q rs specifies how individual i being in state r transfers to state s on two successive occasions. Surely, we can relax the time-homogeneous assumption of transition probabilities by including relevant covariates to interpret the inhomogeneous transition behavior among observation data (see, for example, [12,13,16]) but at the expense of computational burden.…”

Section: Hidden Markov Factor Analysis Modelmentioning

confidence: 99%

“…But more often, particular interest also focuses on exploring the potential heterogeneity of data and investigating its transition pattern over time. In these cases, hidden Markov latent variable model (HMLVM) [10][11][12][13] provides a feasible and unified framework to address these issues. HMLVM assumes that the overall model constitutes the observed process and the underlying hidden state process.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis for Hidden Markov Factor Analysis Models

Xia¹,

Zeng²,

Tang³

2018

New Insights Into Bayesian Inference

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“…For instance, in Vogelsmeier et al (2019) a multivariate continuous outcome is assumed to follow a factor model, with loadings that are state-dependent and follow a latent Markov model. The literature on dynamic dimensionality reduction methods is actually very rich, see for instance Jung et al (2011), Xia et al (2016), Song et al (2017, Bai and Wang (2015), Maruotti et al (2017), Ando and Bai (2017) and Chen et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models

Farcomeni

Ranalli

Viviani

2020

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We present a method for dimension reduction of multivariate longitudinal data, where new variables are assumed to follow a latent Markov model. New variables are obtained as linear combinations of the multivariate outcome as usual. Weights of each linear combination maximize a measure of separation of the latent intercepts, subject to orthogonality constraints. We evaluate our proposal in a simulation study and illustrate it using an EU-level data set on income and living conditions, where dimension reduction leads to an optimal scoring system for material deprivation. An R implementation of our approach can be downloaded from https://github.com/afarcome/LMdim.

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Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models

Cited by 10 publications

References 37 publications

Latent Markov Factor Analysis for Exploring Measurement Model Changes in Time-Intensive Longitudinal Studies

Latent Markov Factor Analysis for Exploring Measurement Model Changes in Time-Intensive Longitudinal Studies

Bayesian Analysis for Hidden Markov Factor Analysis Models

Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models

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