Latent variable models with nonparametric interaction effects of latent variables

2013 ◽  
Vol 33 (10) ◽  
pp. 1723-1737 ◽  
Author(s):  
Xinyuan Song ◽  
Zhaohua Lu ◽  
Xiangnan Feng
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Interaction Effects

Latent variable models are network models

2010 ◽  
Vol 33 (2-3) ◽  
pp. 166-166 ◽  
Author(s):  
Peter C. M. Molenaar
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Network Models ◽  
Latent Variable Models ◽  
Variable Theory ◽  
The Relationship

AbstractCramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

scholarly journals Causal Effects Based on Latent Variable Models

2019 ◽  
Author(s):  
Axel Mayer
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Stochastic Theory ◽  
Latent Variable Models ◽  
Causal Effects ◽  
Equation Modeling ◽  
Differential Effectiveness ◽  
Measurement Models ◽  
Effect Model

Building on the stochastic theory of causal effects and latent state-trait theory, this article shows how a comprehensive analysis of the effectiveness of interventions can be conducted based on latent variable models. The proposed approach offers new ways to evaluate the differential effectiveness of interventions for substantive researchers in experimental and observational studies while allowing for complex measurement models. The key definitions and assumptions of the stochastic theory of causal effects are first introduced and then four statistical models that can be used to estimate various types of causal effects with latent state-trait models are developed and illustrated: The multistate effect model with and without method factors, the true-change effect model, and the multitrait effect model. All effect models with latent variables are implemented based on multigroup structural equation modeling with the EffectLiteR approach. Particular emphasis is placed on the development of models with interactions that allow for interindividual differences in treatment effects based on latent variables. Open source software code is provided for all models.

Comorbidity: A network perspective

2010 ◽  
Vol 33 (2-3) ◽  
pp. 137-150 ◽  
Author(s):  
Angélique O. J. Cramer ◽  
Lourens J. Waldorp ◽  
Han L. J. van der Maas ◽  
Denny Borsboom
Keyword(s):  
Major Depression ◽  
Mental Disorder ◽  
Latent Variables ◽  
Latent Variable ◽  
Latent Variable Models ◽  
The Other ◽  
Generalized Anxiety ◽  
Network Approach ◽  
Variable Theory ◽  
Diagnostic Categories

AbstractThe pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy.

How Are We Testing Interactions in Latent Variable Models? Surging Forward or Fighting Shy?

2019 ◽  
Vol 24 (1) ◽  
pp. 26-54 ◽  
Author(s):  
Jose M. Cortina ◽  
Hannah M. Markell-Goldstein ◽  
Jennifer P. Green ◽  
Yingyi Chang
Keyword(s):  
Present Article ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Interaction Effects ◽  
Analytic Approach ◽  
Rational Basis ◽  
Organizational Science ◽  
Organizational Sciences

Latent variable models and interaction effects have both been common in the organizational sciences for some time. Methods for incorporating interactions into latent variable models have existed since at least Kenny and Judd, and a great many articles and books have developed these methods further. In the present article, we present an empirical review of the methods that organizational science investigators use to test their interaction hypotheses. We show that it is very common for investigators to use fully latent methods to test additive portions of their models, but to abandon such methods when testing the multiplicative portions of their models. By contrast, investigators whose models do not contain interactions tend to stick with fully latent methods throughout. As there is little rational basis for this pattern, it is likely due to continued discomfort regarding the proper application of existing fully latent methods. Thus, we end by offering R code that implements some of the more sophisticated fully latent approaches, and by offering a sequence of decisions that investigators can follow in order to choose the best analytic approach.

scholarly journals Causal Effects Based on Latent Variable Models

Methodology ◽  
2019 ◽  
Vol 15 (Supplement 1) ◽  
pp. 15-28 ◽  
Author(s):  
Axel Mayer
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Stochastic Theory ◽  
Latent Variable Models ◽  
Causal Effects ◽  
Equation Modeling ◽  
Measurement Models ◽  
Effect Model ◽  
Multigroup Structural Equation Modeling

Abstract. Building on the stochastic theory of causal effects and latent state-trait theory, this article shows how a comprehensive analysis of the effects of interventions can be conducted based on latent variable models. The proposed approach offers new ways to evaluate the differential effects of interventions for substantive researchers in experimental and observational studies while allowing for complex measurement models. The key definitions and assumptions of the stochastic theory of causal effects are first introduced and then four statistical models that can be used to estimate various types of causal effects with latent state-trait models are developed and illustrated: The multistate effect model with and without method factors, the true-change effect model, and the multitrait effect model. All effect models with latent variables are implemented based on multigroup structural equation modeling with the EffectLiteR approach. Particular emphasis is placed on the development of models with interactions that allow for interindividual differences in treatment effects based on latent variables. Open source software code is provided for all models.

scholarly journals Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models

2018 ◽  
Author(s):  
Matthew R Whiteway ◽  
Karolina Socha ◽  
Vincent Bonin ◽  
Daniel A Butts
Keyword(s):  
Neural Activity ◽  
Latent Variables ◽  
Latent Variable ◽  
Large Scale ◽  
Latent Variable Models ◽  
Neural Population ◽  
Variable Model ◽  
Experimental Conditions ◽  
Affine Model ◽  
Nonlinear Latent Variable Models

AbstractSensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the information contained in those responses. Such variability is often shared across many neurons, which in principle can allow a decoder to mitigate the effects of such noise, depending on the structure of the shared variability and its relationship to sensory encoding at the population level. Latent variable models offer an approach for characterizing the structure of this shared variability in neural population recordings, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we leverage recent advances in machine learning to introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron’s stimulus selectivity and a set of latent variables that modulate these stimulus responses both additively and multiplicatively. While these approaches can detect general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an “affine model”. In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.

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