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 )

Showing results 21 to 40 of 56.
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  1. Weber, Anett; Steiner, Winfried J.; Lang, Stefan: A comparison of semiparametric and heterogeneous store sales models for optimal category pricing (2017)
  2. Zhang, Wei; Mandal, Abhyuday; Stufken, John: Approximations of the information matrix for a panel mixed logit model (2017)
  3. Fong, Duncan K. H.; Kim, Sunghoon; Chen, Zhe; DeSarbo, Wayne S.: A Bayesian multinomial probit model for the analysis of panel choice data (2016)
  4. Galimberti, Giuliano; Scardovi, Elena; Soffritti, Gabriele: Using mixtures in seemingly unrelated linear regression models with non-normal errors (2016)
  5. Ma, Shaohui; Hou, Lu; Yao, Wensong; Lee, Baozhen: A nonhomogeneous hidden Markov model of response dynamics and mailing optimization in direct marketing (2016)
  6. Benavoli, Alessio; Mangili, Francesca; Ruggeri, Fabrizio; Zaffalon, Marco: Imprecise Dirichlet process with application to the hypothesis test on the probability that (X \leqY) (2015)
  7. Cederburg, Scott; O’Doherty, Michael S.: Asset-pricing anomalies at the firm level (2015)
  8. Mahani, Alireza S.; Sharabiani, Mansour T. A.: SIMD parallel MCMC sampling with applications for big-data Bayesian analytics (2015)
  9. Maldonado, Sebastián; Montoya, Ricardo; Weber, Richard: Advanced conjoint analysis using feature selection via support vector machines (2015)
  10. Müller, Peter; Quintana, Fernando Andrés; Jara, Alejandro; Hanson, Tim: Bayesian nonparametric data analysis (2015)
  11. Rossi, Peter E.: Bayesian non- and semi-parametric methods and applications (2014)
  12. Kim, Jung Seek; Ratchford, Brian T.: A Bayesian multivariate probit for ordinal data with semiparametric random-effects (2013)
  13. Fox, Jeremy T.; Kim, Kyoo il; Ryan, Stephen P.; Bajari, Patrick: The random coefficients logit model is identified (2012)
  14. Karabatsos, George; Walker, Stephen G.: Bayesian nonparametric mixed random utility models (2012)
  15. Vahid Nia; Anthony Davison: High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust (2012) not zbMATH
  16. Adelino Ferreira da Silva: cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis (2011) not zbMATH
  17. Alejandro Jara; Timothy Hanson; Fernando Quintana; Peter Müller; Gary Rosner: DPpackage: Bayesian Semi- and Nonparametric Modeling in R (2011) not zbMATH
  18. Aßmann, Christian; Boysen-Hogrefe, Jens: A Bayesian approach to model-based clustering for binary panel probit models (2011)
  19. Frühwirth-Schnatter, Sylvia: Panel data analysis: a survey on model-based clustering of time series (2011)
  20. Li, Mingliang; Tobias, Justin L.: Bayesian inference in a correlated random coefficients model: modeling causal effect heterogeneity with an application to heterogeneous returns to schooling (2011)