Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models

By L. Corrado. 2005

507 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f666520e0c2bfaf15ab305fa59853e444da83799

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Citations in the corpus

A dimensional analysis of creativity and mental illness: Do anxiety and depression symptoms predict creative cognition, creative accomplishments, and creative self-concepts? (2010, background): “As quantitative psychologists point out, most variables that can be modeled as latent variables should be (e.g., Coffman & McCallum, 2005; Skrondal & Rabe-Hesketh, 2004).”
Count Panel Data (2015, methodology): “This model falls in the class of generalized linear latent and mixed models; see Skrondal and Rabe-Hesketh (2004).”
Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data (2014, unknown): “The model could be extended to allow for random slopes besides the random intercepts in a very parsimonious way [see Goldstein (1995), McCulloch (2008) and Skrondal and Rabe-Hesketh (2004)].”
Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects (2005, background): “They are also useful for inducing dependence between multiple processes as required for selection and endogenous treatment models and their multilevel extensions (e.g. Skrondal and Rabe-Hesketh, 2004, Chapter 14).”
So Many Variables: Joint Modeling in Community Ecology. (2015, methodology): “The variance-covariance matrix of random effects S controls the correlation between taxa, and is assumed to be completely unstructured (apart from constraints on variances needed for presence/absence data [29]).”
On Exact Statistical Properties of Multidimensional Indices Based on Principal Components, Factor Analysis, MIMIC and Structural Equation Models (2008, background): “Though the statistical literature in this area has seen several extensions of the above model with ordinal/categorical variables and/or covariates (exogenous variables) in measurement equations (cf. Muthen 1984, 2002; Jöreskog 2002; Skrondal and Rabe-Hesketh 2004), we will continue with the above fo”
Variable Selection in Finite Mixture of Regression Models (2007, methodology): “The books by Wedel and Kamukura (2000) and Skrondal and Rabe-Hesketh (2004), among others, contain comprehensive reviews on the applications of FMR models in market segmentation and the social sciences.”
Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications (2017, methodology): “Our development is under a generalized latent factor model framework (Skrondal and Rabe-Hesketh, 2004), which extends the generalized linear model framework (McCullagh and Nelder, 1989) to latent factor analysis.”