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Latent variable model

About: Latent variable model is a research topic. Over the lifetime, 3589 publications have been published within this topic receiving 235061 citations.


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Proceedings ArticleDOI
Florian Schmidt1
01 Oct 2019
TL;DR: The authors argue that generalization is the underlying property to address exposure bias and propose unconditional generation as its fundamental benchmark, combining latent variable modeling with a recent formulation of exploration in reinforcement learning to obtain a rigorous handling of true and generated contexts.
Abstract: Exposure bias refers to the train-test discrepancy that seemingly arises when an autoregressive generative model uses only ground-truth contexts at training time but generated ones at test time. We separate the contribution of the learning framework and the model to clarify the debate on consequences and review proposed counter-measures. In this light, we argue that generalization is the underlying property to address and propose unconditional generation as its fundamental benchmark. Finally, we combine latent variable modeling with a recent formulation of exploration in reinforcement learning to obtain a rigorous handling of true and generated contexts. Results on language modeling and variational sentence auto-encoding confirm the model’s generalization capability.

40 citations

Journal ArticleDOI
TL;DR: A two-part model for studying transitions between health states over time when multiple, discrete health indicators are available, which includes a measurement model positing underlying latent health states and a transition model between latent healthStates over time.
Abstract: Summary This paper proposes a two-part model for studying transitions between health states over time when multiple, discrete health indicators are available The includes a measurement model positing underlying latent health states and a transition model between latent health states over time Full maximum likelihood estimation procedures are computationally complex in this latent variable framework, making only a limited class of models feasible and estimation of standard errors problematic For this reason, an estimating equations analogue of the pseudo-likelihood method for the parameters of interest, namely the transition model parameters, is considered The finite sample properties of the proposed procedure are investigated through a simulation study and the importance of choosing strong indicators of the latent variable is demonstrated The applicability of the methodology is illustrated with health survey data measuring disability in the elderly from the Longitudinal Study of Aging

40 citations

Journal ArticleDOI
TL;DR: In this paper, a hybrid discrete choice model framework integrates a latent variable model and a route choice model by combining their measurement and structural equations, and the model is estimated based on data from a laboratory experiment and a field study of a simple network.
Abstract: The purpose of this paper is to demonstrate that latent variables, with the focus on sensation seeking concepts, incorporated in new technique of route choice modeling, improve our analyzing of route choice behavior with pre-trip travel time information. The application of a hybrid discrete choice model framework integrates a latent variable model and a route choice model by combining their measurement and structural equations. The model is estimated based on data from a laboratory experiment and a field study of a simple network. The results show that certain sensation seeking domains (e.g., thrill and adventure seeking) alongside traditional variables (e.g., travel time information) enrich our understanding and provide more insight into route choice behavior. Furthermore, observed personal variables, such as gender and marital status, may serve as causal indicators to sensation seeking variables.

39 citations

Journal ArticleDOI
TL;DR: An empirical application to the repeated measurement of mood states revealed that a model with 2 latent classes fits the data well and the appropriateness of the parameter estimates of this model depends on number of observations and number of occasions.
Abstract: Extensions of latent state-trait models for continuous observed variables to mixture latent state-trait models with and without covariates of change are presented that can separate individuals differing in their occasion-specific variability. An empirical application to the repeated measurement of mood states (N = 501) revealed that a model with 2 latent classes fits the data well. The larger class (76%) consists of individuals whose mood is highly variable, whose general well-being is comparatively lower, and whose mood variability is influenced by daily hassles and uplifts. The smaller class (24%) represents individuals who are rather stable and happier and whose mood is influenced only by daily uplifts but not by daily hassles. A simulation study on the model without covariates with 5 sets of sample sizes and 5 sets of number of occasions revealed that the appropriateness of the parameter estimates of this model depends on number of observations (the higher the better) and number of occasions (the higher the better). Another simulation study estimated Type I and II errors of the Lo-Mendell-Rubin test.

39 citations

Journal ArticleDOI
TL;DR: The latent variables and errors of the Lisrel model are indeterminate even when the parameters of the model are perfectly identified; the degree of indeterminacy of the latent variables depends on the data.
Abstract: The latent variables and errors of the Lisrel model are indeterminate even when the parameters of the model are perfectly identified. The reason for the indeterminacy is that the Lisrel model gives a solution in terms of estimation of latent variables by means of observed variables. The indeterminacy is relevant also in practice; the minimum correlation between equivalent latent variables, is often negative in empirical examples. The degree of indeterminacy of the latent variables depends on the data. The average minimum correlation is a linear combination of the eigenvalues of the correlation matrix of solutions and it is always included in weak bounds which depend on the same eigenvalues.

39 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202375
2022143
2021137
2020185
2019142
2018159