Modeling Household Purchase Behavior with Logistic Normal Regression

By Greg M. Allenby, Peter J. Lenk. 1994

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Citations in the corpus

Panel Data Discrete Choice Models of Consumer Demand (2013, background): “We can gain some valuable intuition into the nature of state dependence by considering the linear case.”
Multilevel logistic regression for polytomous data and rankings (2003, background): “Unobserved Heterogeneity between Clusters and between Units within Clusters Allenby and Lenk (1994) is the only contribution we are aware of modeling dependence at both unit and cluster levels although their model does not include explicit terms representing unit level heterogeneity.”
Modeling Variation in Brand Preference: The Roles of Objective Environment and Motivating Conditions (2002, background): “…stream of research has documented intraindividual variation in brand preference (McAlister and Pessemier 1982, McAlister 1982, Kahn et al. 1986, Allenby and Lenk 1994, Erdem 1996, Yang and Allenby 2000) due to variety seeking, carryover, state dependence, and variables such as price, but…”
A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models (2012, unknown): “…vectors for complicated static discrete choice models, making the posterior mean an attractive estimator compared with classical point estimates in that setting (Albert and Chib 1993; McCulloch and Rossi 1994; Allenby and Lenk 1994; Allenby 1994; Rossi et al. 1996; Allenby and Rossi 1999).”
Profile Construction in Experimental Choice Designs for Mixed Logit Models (2002, methodology): “Such models have been applied to the analysis of scanner panel data (e.g., Allenby and Lenk 1994, 1995), but applications to conjoint choice experiments are less common.”
Modeling Multiple Sources of State Dependence in Random Utility Models: A Distributed Lag Approach (2004, background): “…proposed model accounts for state dependence1 in as complete a manner as possible, and generalizes previously proposed state-dependence models such as Allenby and Lenk (1994), Roy et al. (1996), Keane (1997), etc. that have accounted for only a subset of the effects captured in our model.”
A hierarchical Bayes model of choice between supermarket formats (2003, methodology): “Thus Bayesian analysis using Gibbs sampling has recently provided a way to estimate stable individual choice models (Albert and Chib, 1993; Chib et al., 1998; Allenby and Lenk, 1994; Allenby and Rossi, 1999; McCulloch et al., 2000).”
Variational Inference for Large-Scale Models of Discrete Choice (2007, background): “agents (Train 2003), which is infeasible for large-scale problems. The usual approach to the fully Bayesian random utility model is Markov chain Monte Carlo (MCMC) simulation (Albert and Chib 1993; 2 Allenby and Lenk 1994; Allenby and Rossi 1999). MCMC provides approximate draws from the joint poste”
Discrete Choice Methods with Simulation by Kenneth E. Train (2003, background): “Prominent applications include Beggs et al. (1981), Chapman and Staelin (1982), and Hausman and Ruud (1987).”
The Role of the Management Sciences in Research on Personalization (2003, unknown): “Allenby and Lenk 1994 How can preferences obtained from multiple types of data be combined?”