A Hierarchical Bayes Model of Primary and Secondary Demand

By Neeraj Arora, Greg M. Allenby, James L. Ginter. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, background): “, category purchase; product choice; purchase quantity), such as we consider here, has been explored in several studies [5, 10, 18, 37, 38].”
Structural Workshop Paper - Discrete-Choice Models of Consumer Demand in Marketing (2011, methodology): “This is obtained by integrating (ηK+1, .., ηJ) over the region consistent with no-purchase as per the KKT inequalities in Equation (13).”
The multiple discrete-continuous extreme value (MDCEV) model : Role of utility function parameters, identification considerations, and model extensions (2008, methodology): “The utility function in Equation (1) in such a situation collapses to ∑ k k k x ψ , which represents the perfect substitutes case as proposed by Deaton and Muellbauer (1980) and applied in Hanemann (1984), Chiang (1991), Chintagunta (1993), and Arora et al. (1998), among others.”
Inductive transfer with context-sensitive neural networks (2008, methodology): “Research on MTL has occurred using traditional machine learning methods (Bakker and Heskes 2003; Caruana 1997 ;B axter1997 ;H eskes2000; Thrun and Pratt 1997; Ben-David and Schuller 2003), statistical regression methods (Greene 2002; Zellner 1962; Breiman and Friedman 1998), Bayesian methods involvi”
A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions (2005, methodology): “Further, the MDCEV model has the appealing property that it collapses to the familiar multinomial logit (MNL) choice model in the case of single discreteness, and represents an extension of the single discrete-continuous models of Dubin and McFadden, 1984, Hanemann, 1984, Chiang, 1991, Chintagunta, ”
Policy measures to promote electric mobility – A global perspective (2015, methodology): “Further, CA has frequently been refined over the past few decades, and one of the prevailing methods is the choice-based conjoint analysis with hierarchical Bayesian estimation (CBC/HB; Andrews et al., 2002; Arora et al., 1998; Lenk et al., 1996).”
Regularized multi--task learning (2004, methodology): “We follow the intuition of Hierarchical Bayes [1, 2, 14].”
Customizing Promotions in Online Stores (2004, methodology): “Hanemann (1984), Chiang (1991), Chintagunta (1993), and Arora et al. (1998) modeled the incidence/ choice and quantity decisions jointly by deriving the components from a single utility maximization framework.”
Customer-Specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier (2000, methodology): “4 Instead of using equation (3), draws from h can be obtained with the MetropolisHastings algorithm (Chib and Greenberg, 1995), as in Arora et al. (1998) and Sawtooth Software, Inc.”
The Effectiveness of Demographic and Psychographic Variables for Explaining Brand and Product Category Use (2003, background): “More formally, the rate of consumption of a product is dependent on its utility ðb 0 þ x0bÞ and the utility of all other goods (see Hanneman, 1984; Arora et al., 1998).”
OPTIMAL DESIGNS FOR 2 k PAIRED COMPARISON EXPERIMENTS (2001, background): “Examples of their use in marketing may be found in Chrzan and Orme (2), Haaijer, Wedel, Vriens and Wansbeek (3) and Arora, Allenby and Ginter (4) in transport in Ben-Akiva and Morikawa (5) and Anderson, Borgers, Ettema and Timmermans (6), and in health sciences in Graf, Tanner and Swinyard (7) and L”
Embedded Premium Promotion: Why It Works and How to Make It More Effective (2007, background): “Owing to its heterogeneous motivations, EP effects may not be limited only to capturing brand switchers, but instead could impact primary demand and increase the size of the entire pie ( Arora et al. 1998, Sun 2005).”
Discrete Choice Methods with Simulation by Kenneth E. Train (2003, methodology): “This model has been applied by many researchers in a variety of situations, including energy, transportation, housing, telecommunications, and a host of other fields; see, for example, Ben-Akiva (1973), Train (1986, Chapter 8), Train et al.”
Willingness-to-Pay Estimation with Mixed Logit Models: Some New Evidence (2005, background): “From these values a sampling distribution for h( bn j yn , b, R) can be built, and inferences about the mean and standard deviation values can be obtained (Arora et al, 1998; Sawtooth Software, 1999).”