Factor analysis with (mixed) observed and latent variables in the exponential family

By M. Wedel, Wagner A. Kamakura. 2001

83 citations recorded by the source corpus.

Subfields: Food Science, Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/148cc437d85dccd57e53b9245cac8ebb52810656

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Citations in the corpus

Variational learning for latent Gaussian model of discrete data (2012, background): “, 2011; Tipping and Bishop, 1999], binary and multinomial factor analysis for discrete data [Collins et al., 2002; Khan et al., 2010; Mohamed et al., 2008; Wedel and Kamakura, 2001], and Gaussian process regression and classification [Nickisch and Rasmussen, 2008].”
Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models (2011, methodology): “…models that includes factor analysis and probabilistic principal components analysis for continuous data (Tipping & Bishop, 1999), as well as binary and multinomial factor analysis for discrete data (Wedel & Kamakura, 2001; Collins et al., 2002; Mohamed et al., 2008; Khan et al., 2010).”
Cross-Selling Through Database Marketing: A Mixed Data Factor Analyzer for Data Augmentation and Prediction (2003, methodology): “Our approach builds on recent work in factor analysis for non-normal variables, in particular that by Bartholomew and Knott (1999), Kamakura and Wedel (2000), Moustaki and Knott (2001), and Wedel and Kamakura (2001).”
Multivariate Analysis of Multiple Response Data (2003, methodology): “If the dimension of the latent covariance matrix is of low dimension, a generalized factor model can be used to estimate the model parameters (Wedel and Kamakura 2001).”
Bayesian Multilevel Analysis and MCMC (2008, methodology): “(Earlier versions of the examples manual for WinBUGS [69] frequently employed Γ (0.”
Variational bounds for mixed-data factor analysis (2010, background): “The FA model can be extended to other members of the exponential family by requiring that the natural (canonical) parameters have the form Wz n + µ [WK01, CDS02, MHG08, LT10].”