頁籤選單縮合
| 題 名 | Empirical Bayes Methods for Estimation and Confidence Intervals in High-Dimensional Problems |
|---|---|
| 作 者 | Ghosh, Dehashis; | 書刊名 | Statistica Sinica |
| 卷 期 | 19:1 2009.01[民98.01] |
| 頁 次 | 頁125-143 |
| 分類號 | 319 |
| 關鍵詞 | Estimation target; Hypothesis testing; James-Stein estimation; Multiple comparisons; Simultaneous inference; |
| 語 文 | 英文(English) |
| 英文摘要 | Abstract: There is much recent interest in statistical methods regarding the false discovery rate (FDR). The literature on this topic has two themes. In the first, authors propose sequential testing procedures that control the false discovery rate. In the second, authors study the procedures involving FDR in a univariate mixture model setting. While this work is useful for the selection of hypotheses, there is interest in estimation as well. We take an Empirical Bayes approach and propose estimators and associated confidence intervals in the multiple testing setting. Our framework is general; the proposed methodology is applied to data from a genome scan in Alzheimer's disease. |
本系統中英文摘要資訊取自各篇刊載內容。