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Journal articles on the topic 'Latent variable models'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Ziegel, Eric R., and J. Loehlin. "Latent Variable Models." Technometrics 35, no. 4 (1993): 465. http://dx.doi.org/10.2307/1270304.

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2

Sarle, Warren S. "Latent Variable Models." Technometrics 31, no. 4 (1989): 484–85. http://dx.doi.org/10.1080/00401706.1989.10488603.

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3

Song, Xinyuan, Zhaohua Lu, and Xiangnan Feng. "Latent variable models with nonparametric interaction effects of latent variables." Statistics in Medicine 33, no. 10 (2013): 1723–37. http://dx.doi.org/10.1002/sim.6065.

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4

Bartolucci, Francesco, Silvia Pandolfi, and Fulvia Pennoni. "Discrete Latent Variable Models." Annual Review of Statistics and Its Application 9, no. 1 (2022): 425–52. http://dx.doi.org/10.1146/annurev-statistics-040220-091910.

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We review the discrete latent variable approach, which is very popular in statistics and related fields. It allows us to formulate interpretable and flexible models that can be used to analyze complex datasets in the presence of articulated dependence structures among variables. Specific models including discrete latent variables are illustrated, such as finite mixture, latent class, hidden Markov, and stochastic block models. Algorithms for maximum likelihood and Bayesian estimation of these models are reviewed, focusing, in particular, on the expectation–maximization algorithm and the Markov

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5

Clogg, Clifford C., and Ton Heinen. "Discrete Latent Variable Models." Journal of the American Statistical Association 89, no. 427 (1994): 1141. http://dx.doi.org/10.2307/2290950.

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6

Liao, Tim Futing, and Ton Heinen. "Discrete Latent Variable Models." Contemporary Sociology 23, no. 6 (1994): 895. http://dx.doi.org/10.2307/2076117.

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7

IRINCHEEVA, IRINA, EVA CANTONI, and MARC G. GENTON. "Generalized Linear Latent Variable Models with Flexible Distribution of Latent Variables." Scandinavian Journal of Statistics 39, no. 4 (2012): 663–80. http://dx.doi.org/10.1111/j.1467-9469.2011.00777.x.

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8

Molenaar, Peter C. M. "Latent variable models are network models." Behavioral and Brain Sciences 33, no. 2-3 (2010): 166. http://dx.doi.org/10.1017/s0140525x10000798.

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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.

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9

Eickhoff, Jens C., and Yasuo Amemiya. "Latent variable models for misclassified polytomous outcome variables." British Journal of Mathematical and Statistical Psychology 58, no. 2 (2005): 359–75. http://dx.doi.org/10.1348/000711005x64970.

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10

Kvalheim, Olav M., Reidar Arneberg, Olav Bleie, Tarja Rajalahti, Age K. Smilde, and Johan A. Westerhuis. "Variable importance in latent variable regression models." Journal of Chemometrics 28, no. 8 (2014): 615–22. http://dx.doi.org/10.1002/cem.2626.

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11

Dayton, C. Mitchell, and George B. Macready. "Concomitant-Variable Latent-Class Models." Journal of the American Statistical Association 83, no. 401 (1988): 173–78. http://dx.doi.org/10.1080/01621459.1988.10478584.

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12

Levine, Michael V. "Dimension in latent variable models." Journal of Mathematical Psychology 47, no. 4 (2003): 450–66. http://dx.doi.org/10.1016/s0022-2496(03)00032-4.

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13

Sardy, Sylvain, and Maria-Pia Victoria-Feser. "Isotone additive latent variable models." Statistics and Computing 22, no. 2 (2011): 647–59. http://dx.doi.org/10.1007/s11222-011-9262-z.

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14

Lubke, Gitta H., and Justin Luningham. "Fitting latent variable mixture models." Behaviour Research and Therapy 98 (November 2017): 91–102. http://dx.doi.org/10.1016/j.brat.2017.04.003.

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15

Willems, J. C., and J. W. Nieuwenhuis. "Continuity of latent variable models." IEEE Transactions on Automatic Control 36, no. 5 (1991): 528–38. http://dx.doi.org/10.1109/9.76359.

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16

Hu, Taizhong, Jing Chen, and Chaode Xie. "REGRESSION DEPENDENCE IN LATENT VARIABLE MODELS." Probability in the Engineering and Informational Sciences 20, no. 2 (2006): 363–79. http://dx.doi.org/10.1017/s0269964806060220.

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Three new notions of positive dependence (positive regression dependence, positive left-tail regression dependence, and positive right-tail regression dependence) are studied in this article. Consider a latent variable model where the manifest random variables T1,T2,…,Tn given latent random variable/vector (Θ1,…,Θm) are conditional independent. Conditions are identified under which T1,…,Tn possesses the new dependence notions for different types of latent variable model. Applications of the results are also provided.

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17

RASTELLI, RICCARDO, NIAL FRIEL, and ADRIAN E. RAFTERY. "Properties of latent variable network models." Network Science 4, no. 4 (2016): 407–32. http://dx.doi.org/10.1017/nws.2016.23.

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AbstractWe derive properties of latent variable models for networks, a broad class of models that includes the widely used latent position models. We characterize several features of interest, with particular focus on the degree distribution, clustering coefficient, average path length, and degree correlations. We introduce the Gaussian latent position model, and derive analytic expressions and asymptotic approximations for its network properties. We pay particular attention to one special case, the Gaussian latent position model with random effects, and show that it can represent the heavy-ta

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18

Mislevy, Robert J., and Kathleen M. Sheehan. "Information Matrices in Latent-Variable Models." Journal of Educational Statistics 14, no. 4 (1989): 335–50. http://dx.doi.org/10.3102/10769986014004335.

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The Fisher, or expected, information matrix for the parameters in a latent-variable model is bounded from above by the information that would be obtained if the values of the latent variables could also be observed. The difference between this upper bound and the information in the observed data is the “missing information.” This paper explicates the structure of the expected information matrix and related information matrices, and characterizes the degree to which missing information can be recovered by exploiting collateral variables for respondents. The results are illustrated in the contex

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19

Goldstein, H., and B. S. Everitt. "An Introduction to Latent Variable Models." Biometrics 41, no. 3 (1985): 811. http://dx.doi.org/10.2307/2531303.

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20

Gibbons, Robert D., and D. J. Bartholomew. "Latent Variable Models and Factor Analysis." Journal of the American Statistical Association 83, no. 404 (1988): 1221. http://dx.doi.org/10.2307/2290173.

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21

Sammel, Mary Dupuis, and Louise M. Ryan. "Latent Variable Models with Fixed Effects." Biometrics 52, no. 2 (1996): 650. http://dx.doi.org/10.2307/2532903.

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22

Lipovetsky, Stan. "Latent Variable Models and Factor Analysis." Technometrics 43, no. 1 (2001): 111. http://dx.doi.org/10.1198/tech.2001.s568.

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23

SCHOENBERG, RONALD, and GERHARD ARMINGER. "Latent Variable Models of Dichotomous Data." Sociological Methods & Research 18, no. 1 (1989): 164–82. http://dx.doi.org/10.1177/0049124189018001006.

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24

Mei, Jonathan, and Jose M. F. Moura. "SILVar: Single Index Latent Variable Models." IEEE Transactions on Signal Processing 66, no. 11 (2018): 2790–803. http://dx.doi.org/10.1109/tsp.2018.2818075.

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25

Jackson, J. Edward. "Latent Variable Models and Factor Analysis." Technometrics 31, no. 2 (1989): 266–67. http://dx.doi.org/10.1080/00401706.1989.10488532.

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26

Kukuk, M. "Distributional aspects in latent variable models." Statistical Papers 35, no. 1 (1994): 231–42. http://dx.doi.org/10.1007/bf02926416.

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27

Zhang, Q., and E. H. Ip. "Variable assessment in latent class models." Computational Statistics & Data Analysis 77 (September 2014): 146–56. http://dx.doi.org/10.1016/j.csda.2014.02.017.

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28

Levine, David K. "Reverse regression for latent-variable models." Journal of Econometrics 32, no. 2 (1986): 291–92. http://dx.doi.org/10.1016/0304-4076(86)90042-4.

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29

Kvalheim, Olav M., and Terje V. Karstang. "Interpretation of latent-variable regression models." Chemometrics and Intelligent Laboratory Systems 7, no. 1-2 (1989): 39–51. http://dx.doi.org/10.1016/0169-7439(89)80110-8.

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30

Jolliffe, Ian, and D. J. Bartholomew. "Latent Variable Models and Factor Analysis." Applied Statistics 38, no. 3 (1989): 521. http://dx.doi.org/10.2307/2347739.

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31

Tatsuoka, Kikumi, and David J. Bartholomew. "Latent Variable Models and Factor Analysis." Journal of Educational Statistics 14, no. 1 (1989): 114. http://dx.doi.org/10.2307/1164730.

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32

Mislevy, Robert J., and Kathleen M. Sheehan. "Information Matrices in Latent-Variable Models." Journal of Educational Statistics 14, no. 4 (1989): 335. http://dx.doi.org/10.2307/1164943.

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33

Lenk, Peter J., and B. S. Everitt. "An Introduction to Latent Variable Models." Journal of the American Statistical Association 81, no. 396 (1986): 1123. http://dx.doi.org/10.2307/2289105.

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34

Skinner, C. J., and B. S. Everitt. "An Introduction to Latent Variable Models." Journal of the Royal Statistical Society. Series A (General) 148, no. 2 (1985): 167. http://dx.doi.org/10.2307/2981954.

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35

Rigdon, Edward E. "Advances in Latent Variable Mixture Models." Structural Equation Modeling: A Multidisciplinary Journal 17, no. 2 (2010): 350–54. http://dx.doi.org/10.1080/10705511003661595.

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36

Thomas, Duncan C. "Multistage sampling for latent variable models." Lifetime Data Analysis 13, no. 4 (2007): 565–81. http://dx.doi.org/10.1007/s10985-007-9061-1.

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37

Zhang, Nevin L., Thomas D. Nielsen, and Finn V. Jensen. "Latent variable discovery in classification models." Artificial Intelligence in Medicine 30, no. 3 (2004): 283–99. http://dx.doi.org/10.1016/j.artmed.2003.11.004.

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38

Nounou, Mohamed N., and Hazem N. Nounou. "Multiscale Latent Variable Regression." International Journal of Chemical Engineering 2010 (2010): 1–8. http://dx.doi.org/10.1155/2010/935315.

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Multiscale wavelet-based representation of data has been shown to be a powerful tool in feature extraction from practical process data. In this paper, this characteristic of multiscale representation is utilized to improve the prediction accuracy of some of the latent variable regression models, such as Principal Component Regression (PCR) and Partial Least Squares (PLS), by developing a multiscale latent variable regression (MSLVR) modeling algorithm. The idea is to decompose the input-output data at multiple scales using wavelet and scaling functions, construct multiple latent variable regre

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39

Reuning, Kevin, Michael R. Kenwick, and Christopher J. Fariss. "Exploring the Dynamics of Latent Variable Models." Political Analysis 27, no. 4 (2019): 503–17. http://dx.doi.org/10.1017/pan.2019.1.

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Researchers face a tradeoff when applying latent variable models to time-series, cross-sectional data. Static models minimize bias but assume data are temporally independent, resulting in a loss of efficiency. Dynamic models explicitly model temporal data structures, but smooth estimates of the latent trait across time, resulting in bias when the latent trait changes rapidly. We address this tradeoff by investigating a new approach for modeling and evaluating latent variable estimates: a robust dynamic model. The robust model is capable of minimizing bias and accommodating volatile changes in

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40

Rabe-Hesketh, Sophia, and Anders Skrondal. "Classical latent variable models for medical research." Statistical Methods in Medical Research 17, no. 1 (2008): 5–32. http://dx.doi.org/10.1177/0962280207081236.

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Latent variable models are commonly used in medical statistics, although often not referred to under this name. In this paper we describe classical latent variable models such as factor analysis, item response theory, latent class models and structural equation models. Their usefulness in medical research is demonstrated using real data. Examples include measurement of forced expiratory flow, measurement of physical disability, diagnosis of myocardial infarction and modelling the determinants of clients' satisfaction with counsellors' interviews.

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41

Mayer, Axel. "Causal Effects Based on Latent Variable Models." Methodology 15, Supplement 1 (2019): 15–28. http://dx.doi.org/10.1027/1614-2241/a000174.

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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 variou

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42

Hutchinson, Rebecca, Li-Ping Liu, and Thomas Dietterich. "Incorporating Boosted Regression Trees into Ecological Latent Variable Models." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1343–48. http://dx.doi.org/10.1609/aaai.v25i1.7801.

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Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit models of the ecological phenomenon of interest and the process by which it is observed. However, existing latent variable methods rely on hand-formulated parametric models, which are expensive to design and require extensive preprocessing of the data. Nonparametric methods (such as regression trees) automate these decisions and produce highly accurate models. However, existing tree methods learn direct mappings from input

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43

Shaw, Brian P. "Unobserved Potential: Latent Variables and Music Education Research." Bulletin of the Council for Research in Music Education, no. 234 (October 1, 2022): 45–62. http://dx.doi.org/10.5406/21627223.234.03.

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Abstract This article describes several latent variable approaches that can support rigorous quantitative inquiry in music education. I provide a definition of latent variables, list several advantages associated with their use for the measurement of constructs, and review three types of latent variables featuring utility for music education scholars: factor models, item response models, and latent class (mixture) models. Each type of model is illustrated with exemplar studies from music education literature. In addition, I report the results of a systematic analysis of latent variables’ use i

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44

Jha, Aditi, Zoe C. Ashwood, and Jonathan W. Pillow. "Active Learning for Discrete Latent Variable Models." Neural Computation 36, no. 3 (2024): 437–74. http://dx.doi.org/10.1162/neco_a_01646.

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Abstract Active learning seeks to reduce the amount of data required to fit the parameters of a model, thus forming an important class of techniques in modern machine learning. However, past work on active learning has largely overlooked latent variable models, which play a vital role in neuroscience, psychology, and a variety of other engineering and scientific disciplines. Here we address this gap by proposing a novel framework for maximum-mutual-information input selection for discrete latent variable regression models. We first apply our method to a class of models known as mixtures of lin

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45

Rule, Michael E., Martino Sorbaro, and Matthias H. Hennig. "Optimal Encoding in Stochastic Latent-Variable Models." Entropy 22, no. 7 (2020): 714. http://dx.doi.org/10.3390/e22070714.

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In this work we explore encoding strategies learned by statistical models of sensory coding in noisy spiking networks. Early stages of sensory communication in neural systems can be viewed as encoding channels in the information-theoretic sense. However, neural populations face constraints not commonly considered in communications theory. Using restricted Boltzmann machines as a model of sensory encoding, we find that networks with sufficient capacity learn to balance precision and noise-robustness in order to adaptively communicate stimuli with varying information content. Mirroring variabili

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46

Severson, Kristen A., Soumya Ghosh, and Kenney Ng. "Unsupervised Learning with Contrastive Latent Variable Models." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4862–69. http://dx.doi.org/10.1609/aaai.v33i01.33014862.

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In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings. However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriche

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47

Qu, Yinsheng, Marion R. Piedmonte, and Sharon V. Medendorp. "Latent Variable Models for Clustered Ordinal Data." Biometrics 51, no. 1 (1995): 268. http://dx.doi.org/10.2307/2533332.

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48

Huber, Philippe, Elvezio Ronchetti, and Maria-Pia Victoria-Feser. "Estimation of generalized linear latent variable models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66, no. 4 (2004): 893–908. http://dx.doi.org/10.1111/j.1467-9868.2004.05627.x.

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49

Ghosh, Joyee, Amy H. Herring, and Anna Maria Siega-Riz. "Bayesian Variable Selection for Latent Class Models." Biometrics 67, no. 3 (2010): 917–25. http://dx.doi.org/10.1111/j.1541-0420.2010.01502.x.

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50

Shashanka, Madhusudana, Bhiksha Raj, and Paris Smaragdis. "Probabilistic Latent Variable Models as Nonnegative Factorizations." Computational Intelligence and Neuroscience 2008 (2008): 1–8. http://dx.doi.org/10.1155/2008/947438.

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This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order decompositions and sparsity constraints. We argue through these extensions that the use of this approach allows for rapid development of complex statistical models for analyzing nonnegative data.

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