Multivariate Analysis of Multiple Response Data
By Yancy D. Edwards, Greg M. Allenby. 2003
210 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/351c72428d8bdfb956756f78e0de8bf9c68ddcb9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jung-Sun Shin
- M. Wedel
- Yang Yang
- Thomas Otter
- Andrew Ainslie
- Chandra R. Bhat
- Garrett P. Sonnier
- Gang Li
In Statistics and Probability
- Jung-Sun Shin
- M. Wedel
- Yang Yang
- Thomas Otter
- Andrew Ainslie
- Chandra R. Bhat
- Philip Hans Franses
- Richard Paap
Citations in the corpus
- Retrieving Unobserved Consideration Sets from Household Panel Data (2005, background): “Therefore, it may be surprising that no research to date has addressed the issue of convergent validity of stated versus revealed consideration sets.”
- Where Can Tourism-Led Growth and Economy-Driven Tourism Growth Occur? (2019, methodology): “Following the procedure documented by Edwards and Allenby (2003), we used Gibbs sampling, a Markov chain Monte Carlo (MCMC) algorithm, to obtain a sequence of observations when a stationary distribution is converged independently from the initial status.”
- Heterogeneity distributions of willingness-to-pay in choice models (2007, methodology): “The researcher is free to empirically explore the posterior distribution of the transformation by simply computing the transformation on each iteration of the sampler (e.g., Edwards and Allenby 2003 ).”
- A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions (2005, background): “Thus, the central basis of the activityof Manchanda et al. (1999), Baltas (2004), Edwards and Allenby (2003), and Bhat and Srinivasan (2004).”
- Consumer preferences and willingness to pay for advanced vehicle technology options and fuel types (2015, methodology): “1 The Multiple Discrete-Continuous Probit (MDCP) Model The multivariate logit model and multivariate probit model (Baltas, 2004; Edwards and Allenby, 2003) are approaches that may be considered for modeling multiple discrete choice situations (i.”
- The multiple discrete-continuous extreme value (MDCEV) model : Role of utility function parameters, identification considerations, and model extensions (2008, methodology): “COMPARISON WITH EARLIER MULTIPLE DISCRETE-CONTINUOUS MODELS In this section, we discuss how the model developed in this paper differs from the model of Kim et al. (2002), those in Environmental Economics, and the earlier models by Bhat and colleagues.”