تقرير
A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family
| العنوان: | A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family |
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| المؤلفون: | Banerjee, Trambak, Liu, Qiang, Mukherjee, Gourab, Sun, Wenguang |
| سنة النشر: | 2019 |
| المجموعة: | Mathematics Statistics |
| مصطلحات موضوعية: | Mathematics - Statistics Theory, Statistics - Methodology |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/1910.08997Test |
| رقم الانضمام: | edsarx.1910.08997 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |