bayesm

Teaching Bayesian statistics to marketing and business students. We discuss our experiences teaching Bayesian statistics to students in doctoral programs in business. These students often have weak backgrounds in mathematical statistics and a predisposition against likelihood-based methods stemming from prior exposure to econometrics. This can be overcome by an intense course that emphasizes the value of the Bayesian approach to solving nontrivial problems. The success of our course is primarily due to the emphasis on statistical computing. This is facilitated by our R package, bayesm, which provides efficient implementation of advanced methods and models.


References in zbMATH (referenced in 56 articles )

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  1. Volinskiy, Dmitriy; Veeman, Michele; Adamowicz, Wiktor: Allocation of public funds to R&D: A portfolio choice-styled decision model and a biotechnology case study (2011)
  2. Hruschka, Harald: Considering endogeneity for optimal catalog allocation in direct marketing (2010)
  3. Kalouptsidis, N.; Psaraki, V.: Approximations of choice probabilities in mixed logit models (2010)
  4. Zellner, Arnold; Ando, Tomohiro: A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model (2010)
  5. Jiang, Renna; Manchanda, Puneet; Rossi, Peter E.: Bayesian analysis of random coefficient logit models using aggregate data (2009)
  6. Conley, Timothy G.; Hansen, Christian B.; McCulloch, Robert E.; Rossi, Peter E.: A semi-parametric Bayesian approach to the instrumental variable problem (2008)
  7. Zhang, Xiao; Boscardin, W. John; Belin, Thomas R.: Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models (2008)
  8. Congdon, Peter: Model weights for model choice and averaging (2007)
  9. Fok, Dennis; Franses, Philip Hans; Paap, Richard: Seasonality and non-linear price effects in scanner-data-based market-response models (2007)
  10. Frühwirth-Schnatter, Sylvia; Frühwirth, Rudolf: Auxiliary mixture sampling with applications to logistic models (2007)
  11. Kim, Jaehwan; Allenby, Greg M.; Rossi, Peter E.: Product attributes and models of multiple discreteness (2007)
  12. Liu, Qing; Dean, Angela M.; Allenby, Greg M.: Design for hyperparameter estimation in linear models (2007)
  13. Zellner, Arnold: Generalizing the standard product rule of probability theory and Bayes’s theorem (2007)
  14. Kosuke Imai; David Van Dyk: MNP: R Package for Fitting the Multinomial Probit Model (2005) not zbMATH
  15. Rossi, Peter E.; Allenby, Greg M.; McCulloch, Robert: Bayesian statistics and marketing (2005)
  16. Allenby, Greg M.; Rossi, Peter E.: Marketing models of consumer heterogeneity (1999)