New website: coxprofs.cox.smu.edu/braunm
The new website will be updated more frequently.
Braun, Michael and Wendy Moe, “Online Display Advertising: Modeling the Effects of Multiple
Creatives and Individual Impression Histories,” Marketing Science 32(5), Sept./Oct. 2013
Braun, Michael and David Schweidel, “Modeling Customer Lifetimes with Multiple Causes of
Churn,” Marketing Science 30(5), 881-902, Sept./Oct. 2011
Braun, Michael and André Bonfrer, “Scalable Inference of Customer Similarities from
Interactions Data using Dirichlet Processes,” Marketing Science 30(3), 513-531, May/June 2011.
Braun, Michael and Jon McAuliffe, “Variational Inference for Large-Scale Models of Discrete
Choice,” Journal of the American Statistical Association, 105(489), 324-335, March 2010.
Urban, Glen L., John R. Hauser, Guilherme Liberali, Michael Braun and Fareena Sultan, “Morph
the Web To Build Empathy, Trust and Sales,” Sloan Management Review, 50(4), Summer 2009.
Hauser, John R., Glen L. Urban, Guilherme Liberali and Michael Braun, “Website Morphing”,
Marketing Science 28(2), Mar./Apr., 2009
Braun, Michael, Peter S. Fader, Eric T. Bradlow and Howard Kunreuther, “Modeling the
‘Pseudodeductible’ in Insurance Claims Decisions,” Management Science 52(8), 1258-1272,
Braun, Michael and Alexander Muermann, “The Impact of Regret on the Demand for
Insurance,” Journal of Risk and Insurance, 71(4), Dec. 2004
“trustOptim: An R Package for Trust Region Optimization with Sparse Hessians.”
Conditionally accepted at Journal of Statistical Software
“Customer Base Analysis with Service Quality Data” (with David Schweidel and Eli Stein).
Under second-round review at Journal of Marketing Research
“Scalable Rejection Sampling for Bayesian Hierarchical Models” (with Paul Damien).
Under second-round review at Marketing Science
(All R packages are available on CRAN at cran.r-project.org)
trustOptim – R package for scalable nonlinear optimization using trust region methods,optimized for objective functions with sparse Hessians (as in hierarchical models).
bayesGDS – R package for estimating Bayesian hierarchical models using Generalized Direct
sparseHessianFD – R package for computing sparse Hessians when only the sparsity structure is known.
sparseMVN - R package for sampling from, and computing the log density of, a multivariate normal distribution for which the covariance or precision matrix is sparse.