Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/211106
Authors: 
Koenker, Roger
Gu, Jiaying
Year of Publication: 
2019
Series/Report no.: 
cemmap working paper No. CWP13/19
Abstract: 
Efron's elegant approach to g-modeling for empirical Bayes problems is contrasted with an implementation of the Kiefer-Wolfowitz nonparametric maximum likelihood estimator for mixture models for several examples. The latter approach has the advantage that it is free of tuning parameters and consequently provides a relatively simple complementary method.
Subjects: 
Nonparametric maximum likelihood
mixture model
convex optimization
Document Type: 
Working Paper

Files in This Item:
File
Size
355.02 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.