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Creators/Authors contains: "Banerjee, Trambak"

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  1. ; ; ; (, Journal of machine learning research)
    Accepted Manuscript Full Text Available
  2. ; ; (, Journal of machine learning research)
    Accepted Manuscript Full Text Available
  3. ; ; (, The Annals of Applied Statistics)
    Full Text Available 
  4. ; ; (, Journal of the American Statistical Association)
    null (Ed.)
    Full Text Available 
  5. ; ; ; (, Journal of the American Statistical Association)
    null (Ed.)
    Full Text Available 
  6. ; ; ; (, Journal of machine learning research)
    null (Ed.)
    We develop a Nonparametric Empirical Bayes (NEB) framework for compound estimation in the discrete linear exponential family, which includes a wide class of discrete distributions frequently arising from modern big data applications. We propose to directly estimate the Bayes shrinkage factor in the generalized Robbins' formula via solving a scalable convex program, which is carefully developed based on a RKHS representation of the Stein's discrepancy measure. The new NEB estimation framework is flexible for incorporating various structural constraints into the data driven rule, and provides a unified approach to compound estimation with both regular and scaled squared error losses. We develop theory to show that the class of NEB estimators enjoys strong asymptotic properties. Comprehensive simulation studies as well as analyses of real data examples are carried out to demonstrate the superiority of the NEB estimator over competing methods. 
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    Accepted Manuscript Full Text Available