Bayesian inference for finite mixtures of generalized linear models with random effects

By Peter J. Lenk, Wayne S. DeSarbo. 2000

185 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/eac8424522cdffd59a2aed3b9b653c963f36e0c4

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Citations in the corpus

Variational learning for latent Gaussian model of discrete data (2012, methodology): “Standard MCMC algorithms have been extensively applied for inference in discrete-data LGMs, for example, Gibbs sampling [Zeger and Karim, 1991], single move adaptive rejection Gibbs sampling [Dellaportas and Smith, 1993], Metropolis Hasting algorithm [Gamerman, 1997; Lenk and DeSarbo, 2000; Rossi an”
Bayesian Analysis of the Heterogeneity Model (2004, methodology): “Bayesian estimation of the heterogeneity model is discussed in Allenby et al. (1998) and Lenk and DeSarbo (2000).”
Latent Variable Modelling: A Survey * (2007, methodology): “Such a model was used by Verbeke & Lesaffre (1996), Allenby et al. (1998) and Lenk & DeSarbo (2000) for random coefficient models, by Magder & Zeger (1996), Carroll et al. (1999) and Richardson et al. (2002) for covariate measurement error models, and Uebersax (1993) and Uebersax & Grove (1993) for…”
Enhancing Marketing with Engineering: Optimal Product Line Design for Heterogeneous Markets (2011, unknown): “We therefore specify a very general continuous distributional form for βi by using a mixture of normal distributions (Lenk & DeSarbo, 2000).”
Bayesian Modelling and Inference on Mixtures of Distributions (2005, methodology): “2001), experimental design and analysis (Nobile and Green 2000, Sebastiani and Wynn 2000), random effects generalised linear models (Lenk and DeSarbo 2000) and binary data (Basu and Mukhopadhyay 2000).”
7. Multilevel Latent Class Models (2003, methodology): “The idea of introducing random effects in LC analysis is not new, as has been shown, for example, in Qu, Tan, and Kutner (1996) and Lenk and DeSarbo (2000).”
Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach (2010, methodology): “individual specic coecients nk is used in each segment, accommodating preference heterogeneity across individuals belonging to that group ( Lenk and DeSarbo, 2000 ).”
Latent Growth Modeling of Longitudinal Data: A Finite Growth Mixture Modeling Approach (2001, background): “As alluded to earlier, developments in mixture modeling have emerged (e.g., Arminger, Stein, Wittenberg, 1999; Jedidi et al., 1997; Lenk & DeSarbo, 2000; Yung, 1997).”
Probabilistic clustering of extratropical cyclones using regression mixture models (2007, methodology): “While a non-diagonal covariance matrix would allow for modeling of dependence between the latitude and longitude measurement noise terms, this level of detailed modeling did not appear necessary for the purposes of clustering and, thus, we used a diagonal covariance assumption as a simpler…”