Constructing confidence intervals for selected parameters
Constructing confidence intervals for selected parameters
复制标题
构建所选参数的置信区间
DOI：
10.1111/biom.13222
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发表时间：
2020
期刊：
影响因子：
1.9
通讯作者：
Xinping Cui
中科院分区：
文献类型：
--
作者：
Haibing Zhao;Xinping Cui
在大规模问题中，通常的做法是通过Benjamini和Hochberg程序和构造置信区间（CI）选择重要参数，以进行进一步研究，而CIS的错误覆盖范围陈述率（FCR）则在CIS上受到控制。所需的水平通过减少数量的选择性CI来产生较短的顺式。对于独立数据，FCR不对称。通过分析HIV研究的微阵列数据，提出的程序。
In large‐scale problems, it is common practice to select important parameters by a procedure such as the Benjamini and Hochberg procedure and construct confidence intervals (CIs) for further investigation while the false coverage‐statement rate (FCR) for the CIs is controlled at a desired level. Although the well‐known BY CIs control the FCR, they are uniformly inflated. In this paper, we propose two methods to construct shorter selective CIs. The first method produces shorter CIs by allowing a reduced number of selective CIs. The second method produces shorter CIs by allowing a prefixed proportion of CIs containing the values of uninteresting parameters. We theoretically prove that the proposed CIs are uniformly shorter than BY CIs and control the FCR asymptotically for independent data. Numerical results confirm our theoretical results and show that the proposed CIs still work for correlated data. We illustrate the advantage of the proposed procedures by analyzing the microarray data from a HIV study.
影响因子：
0.9
作者：
Sarkar, Sanat K.;Guo, Wenge;Finner, Helmut
通讯作者：
Finner, Helmut