Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/211106 
Year of Publication: 
2019
Series/Report no.: 
cemmap working paper No. CWP13/19
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
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
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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