Name
Abstract | ||
|---|---|---|
The general maximum likelihood empirical Bayes (GMLEB) method has been proven to possess optimal properties and demonstrated to have superior numerical performance in the Gaussian sequence model. Although it is known that nonparametric function estimation and the Gaussian sequence models are closely related, implementation of the GMLEB in function estimation problems still awaits careful analysis. In this paper, we consider adaptive estimation to inhomogeneous smoothness. We study the extent to which the optimality properties of the GMLEB can be carried out from the Gaussian sequence model to nonparametric function estimation. We demonstrate the proposed method's superior performance in large sample size settings. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.jmva.2013.07.013 | J. Multivariate Analysis |
Keywords | Field | DocType |
nonparametric function estimation,empirical bayes,adaptive estimation,gaussian sequence model,function estimation problem,nonparametric empirical bayes,minimax estimation,careful analysis,superior numerical performance,superior performance,function estimation,nonparametric regression | Econometrics,Mathematical optimization,Minimax,Nonparametric regression,Maximum likelihood,Nonparametric statistics,Gaussian,Statistics,Smoothness,Sample size determination,Mathematics,Bayes' theorem | Journal |
Volume | ISSN | Citations |
122, | 0047-259X | 1 |
PageRank | References | Authors |
0.98 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenhua Jiang | 1 | 3 | 2.10 |
| Cun-Hui Zhang | 2 | 174 | 18.38 |