Latent Variable Models and Factor Analysis

By Stan Lipovetsky. 2001

879 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Statistics and Probability, Probabilistic & Bayesian Methods

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Citations in the corpus

Beyond CCA: Moment Matching for Multi-View Models (2016, neutral): “Gaussian CCA is subject to some well-known unidentifiability issues, in the same way as the closely related factor analysis model (FA; Bartholomew, 1987; Basilevsky, 1994) and its special case, the probabilistic principal component analysis model (PPCA; Tipping & Bishop, 1999; Roweis, 1998).”
Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models (2014, methodology): “When we have only one layer of latent variables and use a linear mapping T ( · ), we recover factor analysis (Bartholomew & Knott, 1999) – more general mappings allow for a non-linear factor analysis.”
Comparison of multi-subject ICA methods for analysis of fMRI data (2010, methodology): “Because GIFT software implemented GICA2 from versions v1.”
Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications (2017, neutral): “Latent factor models, originated from the seminal work of Spearman (1904) on human intelligence, play a key role in multivariate data analysis (Anderson, 2003; Skrondal and Rabe-Hesketh, 2004; Bartholomew et al., 2008, 2011).”
Multilevel Models for Ordinal and Nominal Variables (2008, background): “Because the errors are assumed to follow a logistic distribution and the random effects a normal distribution, this model and models closely related to it are often referred to as logistic/normal or logit/normit models, especially in the latent trait model literature [11].”
High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature (2005, methodology): “The development of item factor analysis based on item response theory (IRT) and maximum marginal likelihood (MML) estimation of factor loadings was a marked improvement in this respect (Bock and Aitkin, 1981; Bock, Gibbons, and Muraki, 1987; Bartholomew and Knott, 1999). Direct evaluation of the mar”
Factor models for multivariate count data (2003, methodology): “The models we provide extend the general linear latent variable model given in Bartholomew and Knott [17, p. 19, 25, 159] and Wedel and Kamakura [27].”
Structural Equation Modeling: Categorical Variables (2005, background): “, [1], [2], [4], [9], [11], [12] and [13] for theory and applications.”
Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. (2000, methodology): “For example, Bucholz et al. (1996) attempted to identify distinctive subtypes of alcoholics. They applied latent class analysis to a set of lifetime symptoms of alcohol dependence using data from the Collaborative Study of the Genetics of Alcoholism and related the four-class solution to a set of ba”
Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project. (2002, methodology): “We generated “factor 1” and “factor 2” by factor analysis with a maximum likelihood approach (53, 54) applied to inputs listed in table 2 (see second footnote in table), using rank values of the census data, rather than impose arbitrary transformations to normalize their often considerably skewed di”
Measurement errors in multivariate measurement scales (2005, methodology): “The measurement model relates our framework to the factor analysis model [20] and its generalizations [6,10,18], but it is also a multidimensional generalization of the classical, one-dimensional true score model [22, Chapter 3].”
Continuous latent variable models for dimensionality reduction and sequential data reconstruction (2001, methodology): “This is a well-known problem in the literature of factor analysis and finite mixture distributions (Bartholomew, 1987; Everitt and Hand, 1981), called a Heywood case.”
Hidden Talents of the Variational Autoencoder. (2017, background): “Note that the seminal work from [36] mentions in passing that a special case of their VAE decoder model reduces to factor analysis [1], a cousin of probabilistic PCA; however, no rigorous, complementary analysis is provided, such as how latent-space sparsity can emerge as we will introduce shortly.”
A Manifesto on Psychology as Idiographic Science: Bringing the Person Back Into Scientific Psychology, This Time Forever (2004, methodology): “Following the classification of latent variable models in Bartholomew (1987), special freeware is available for the case in which manifest and latent processes are both metrical (state–space modeling; cf.”