Publications

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 AndrĂ© 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,
August 2006
 
Braun, Michael and Alexander Muermann, “The Impact of Regret on the Demand for
Insurance,” Journal of Risk and Insurance, 71(4), Dec. 2004
 
 

Working Papers 

“trustOptim: An R Package for Trust Region Optimization with Sparse Hessians.”
Under third-round review at Journal of Statistical Software
 
“Customer Base Analysis with Service Quality Data” (with David Schweidel and Eli Stein).
Invited revision at Journal of Marketing Research
 
“Scalable Rejection Sampling for Bayesian Hierarchical Models” (with Paul Damien).
Under review at Marketing Science
 
 

Software

(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
Sampling.
 
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.

 

 


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