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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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Journal ArticleDOI
TL;DR: In this article, an alternative procedure is presented that solves the convergence problem of the Joreskog-Yang procedure and provides consistent estimators of the parameters of the Kenny-Judd interaction model.
Abstract: Kenny and Judd (1984) developed a latent variable interaction model for observed variables centered around their population means. They estimated the model by using a covariance matrix calculated from sample-mean-centered variables and products of these variables. Subsequently,Joreskog and Yang (1996) identified the need to include intercepts for the measurement and structural equations and estimated the model by using a covariance matrix calculated from noncentered observed variables and products of these variables, and means of the observed variables and the products of noncentered variables. Evidence is presented that the Joreskog-Yang procedure for estimating the Kenny-Judd interaction model is subject to severe convergence problems when implemented in LISREL8.3 and means for the indicators of the latent exogenous variables are nonzero. An alternative procedure is presented that solves the convergence problem and provides consistent estimators of the parameters.

178 citations

Journal ArticleDOI
TL;DR: A hierarchical Bayesian model is presented that is able to capture higher-order nonlinear structure and represent nonstationary data distributions and Adapting the model to image or audio data yields a nonlinear, distributed code for higher- order statistical regularities that reflect more abstract, invariant properties of the signal.
Abstract: Capturing statistical regularities in complex, high-dimensional data is an important problem in machine learning and signal processing. Models such as principal component analysis (PCA) and independent component analysis (ICA) make few assumptions about the structure in the data and have good scaling properties, but they are limited to representing linear statistical regularities and assume that the distribution of the data is stationary. For many natural, complex signals, the latent variables often exhibit residual dependencies as well as nonstationary statistics. Here we present a hierarchical Bayesian model that is able to capture higher-order nonlinear structure and represent nonstationary data distributions. The model is a generalization of ICA in which the basis function coefficients are no longer assumed to be independent; instead, the dependencies in their magnitudes are captured by a set of density components. Each density component describes a common pattern of deviation from the marginal density of the pattern ensemble; in different combinations, they can describe nonstationary distributions. Adapting the model to image or audio data yields a nonlinear, distributed code for higher-order statistical regularities that reflect more abstract, invariant properties of the signal.

177 citations

Proceedings ArticleDOI
16 Jun 2012
TL;DR: A new latent variable model for scene recognition that represents a scene as a collection of region models arranged in a reconfigurable pattern and uses a latent variable to specify which region model is assigned to each image region.
Abstract: We propose a new latent variable model for scene recognition. Our approach represents a scene as a collection of region models (“parts”) arranged in a reconfigurable pattern. We partition an image into a predefined set of regions and use a latent variable to specify which region model is assigned to each image region. In our current implementation we use a bag of words representation to capture the appearance of an image region. The resulting method generalizes a spatial bag of words approach that relies on a fixed model for the bag of words in each image region. Our models can be trained using both generative and discriminative methods. In the generative setting we use the Expectation-Maximization (EM) algorithm to estimate model parameters from a collection of images with category labels. In the discriminative setting we use a latent structural SVM (LSSVM). We note that LSSVMs can be very sensitive to initialization and demonstrate that generative training with EM provides a good initialization for discriminative training with LSSVM.

177 citations

DOI
11 Jan 2011
TL;DR: This chapter shows that already in the traditional multilevel analysis areas of regression and growth there are several new modeling opportunities that should be considered and gives an overview with examples of multileVEL modeling for path analysis, factor analysis, structural equation modeling, and growth mixture modeling.
Abstract: The outline of the chapter is as follows. Section 2.2 discusses two extensions of two-level regression analysis, Section 2.3 discusses two-level path analysis and structural equation modeling, Section 2.4 presents an example of two-level exploratory factor analysis (EFA), Section 2.5 discusses two-level growth modeling using a two-part model, Section 2.6 discusses an unconventional approach to three-level growth modeling, and Section 2.7 presents an example of multilevel growth mixture modeling.

176 citations

Journal ArticleDOI
TL;DR: A model‐based approach to unconstrained ordination is proposed based on finite mixture models and latent variable models, capable of handling different data types and different forms of species response to latent gradients.
Abstract: Summary Unconstrained ordination is commonly used in ecology to visualize multivariate data, in particular, to visualize the main trends between different sites in terms of their species composition or relative abundance. Methods of unconstrained ordination currently used, such as non-metric multidimensional scaling, are algorithm-based techniques developed and implemented without directly accommodating the statistical properties of the data at hand. Failure to account for these key data properties can lead to misleading results. A model-based approach to unconstrained ordination can address this issue, and in this study, two types of models for ordination are proposed based on finite mixture models and latent variable models. Each method is capable of handling different data types and different forms of species response to latent gradients. Further strengths of the models are demonstrated via example and simulation. Advantages of model-based approaches to ordination include the following: residual analysis tools for checking assumptions to ensure the fitted model is appropriate for the data; model selection tools to choose the most appropriate model for ordination; methods for formal statistical inference to draw conclusions from the ordination; and improved efficiency, that is model-based ordination better recovers true relationships between sites, when used appropriately.

175 citations


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