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Journal of the Royal Statistical Society Series B-Statistical Methodology最新文献

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Junhui Cai, Dan Yang, Linda Zhao and Wu Zhu’s contribution to the Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe & Zeng 蔡俊辉、杨丹、赵琳达、朱武对Rohe & Zeng“用方差进行统计推理的复古因子分析”讨论的贡献
IF 5.8 1区 数学 Q1 STATISTICS & PROBABILITY
Journal of the Royal Statistical Society Series B-Statistical Methodology
Pub Date : 2023-04-05 DOI: 10.1093/jrsssb/qkad038
Junhui Cai, Dan Yang, Linda H. Zhao, Wu Zhu
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引用次数: 0
Kaizheng Wang’s contribution to the Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe & Zeng 王开正对Rohe & Zeng“用方差进行统计推理的复古因子分析”讨论的贡献
IF 5.8 1区 数学 Q1 STATISTICS & PROBABILITY
Journal of the Royal Statistical Society Series B-Statistical Methodology
Pub Date : 2023-04-05 DOI: 10.1093/jrsssb/qkad033
Kaizheng Wang
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引用次数: 0
Xiaoyue Niu’s contribution to the Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe & Zeng 牛晓月对Rohe & Zeng“Vintage Factor Analysis with Varimax执行Statistical Inference”讨论的贡献
IF 5.8 1区 数学 Q1 STATISTICS & PROBABILITY
Journal of the Royal Statistical Society Series B-Statistical Methodology
Pub Date : 2023-04-05 DOI: 10.1093/jrsssb/qkad043
Xiaoyue Niu
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引用次数: 0
Joshua Cape’s contribution to the Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe & Zeng Joshua Cape对Rohe & Zeng的“Vintage Factor Analysis with variimax perform Statistical Inference”讨论的贡献
IF 5.8 1区 数学 Q1 STATISTICS & PROBABILITY
Journal of the Royal Statistical Society Series B-Statistical Methodology
Pub Date : 2023-04-05 DOI: 10.1093/jrsssb/qkad032
J. Cape
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引用次数: 0
Proposer of the vote of thanks to Rohe & Zeng and contribution to the Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” 向Rohe & Zeng投感谢票并参与讨论“Vintage Factor Analysis with variimax执行统计推断”
IF 5.8 1区 数学 Q1 STATISTICS & PROBABILITY
Journal of the Royal Statistical Society Series B-Statistical Methodology
Pub Date : 2023-04-04 DOI: 10.1093/jrsssb/qkad030
P. Hoff
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引用次数: 0
Yinqiu He, Yuqi Gu and Zhilian Ying’s contribution to the Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe & Zeng 何银秋、顾玉琪、应之莲对Rohe & Zeng“用方差进行统计推理的复古因子分析”讨论的贡献
IF 5.8 1区 数学 Q1 STATISTICS & PROBABILITY
Journal of the Royal Statistical Society Series B-Statistical Methodology
Pub Date : 2023-04-04 DOI: 10.1093/jrsssb/qkad036
He Yinqiu, Gu Yuqi, Yin Zhiliang
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引用次数: 0
Discussion: “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe and Zeng 讨论:Rohe和Zeng的“用方差进行统计推断的复古因子分析”
IF 5.8 1区 数学 Q1 STATISTICS & PROBABILITY
Journal of the Royal Statistical Society Series B-Statistical Methodology
Pub Date : 2023-04-04 DOI: 10.1093/jrsssb/qkad040
Yunxiao Chen, Gongjun Xu
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引用次数: 0
Christine P Chai's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng Christine P Chai对Rohe &amp讨论“Vintage Factor Analysis with variimax perform Statistical Inference”的贡献;曾
1区 数学 Q1 STATISTICS & PROBABILITY
Journal of the Royal Statistical Society Series B-Statistical Methodology
Pub Date : 2023-04-04 DOI: 10.1093/jrsssb/qkad039
Christine P Chai
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引用次数: 0
Comments on the paper “Vintage Factor Analysis with varimax Performs Statistical Inference” by Karl Rohe and Muzhe Zeng 对卡尔·罗、曾慕哲《用方差进行统计推断的复古因子分析》一文的评析
IF 5.8 1区 数学 Q1 STATISTICS & PROBABILITY
Journal of the Royal Statistical Society Series B-Statistical Methodology
Pub Date : 2023-04-03 DOI: 10.1093/jrsssb/qkad041
K. Kumar
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引用次数: 0
Estimating the Efficiency Gain of Covariate-Adjusted Analyses in Future Clinical Trials Using External Data. 利用外部数据估计协变量调整分析在未来临床试验中的效率增益。
IF 5.8 1区 数学 Q1 STATISTICS & PROBABILITY
Journal of the Royal Statistical Society Series B-Statistical Methodology
Pub Date : 2023-04-01 DOI: 10.1093/jrsssb/qkad007
Xiudi Li, Sijia Li, Alex Luedtke

We present a framework for using existing external data to identify and estimate the relative efficiency of a covariate-adjusted estimator compared to an unadjusted estimator in a future randomized trial. Under conditions, these relative efficiencies approximate the ratio of sample sizes needed to achieve a desired power. We develop semiparametrically efficient estimators of the relative efficiencies for several treatment effect estimands of interest with either fully or partially observed outcomes, allowing for the application of flexible statistical learning tools to estimate the nuisance functions. We propose an analytic Wald-type confidence interval and a double bootstrap scheme for statistical inference. We demonstrate the performance of the proposed methods through simulation studies and apply these methods to estimate the efficiency gain of covariate adjustment in Covid-19 therapeutic trials.

我们提出了一个框架，用于使用现有的外部数据来识别和估计在未来的随机试验中，与未调整的估计量相比，协变量调整估计量的相对效率。在一定条件下，这些相对效率近似于达到所需功率所需的样品大小之比。我们开发了具有完全或部分观察结果的几种治疗效果估计的相对效率的半参数有效估计器，允许应用灵活的统计学习工具来估计干扰函数。我们提出了一个分析的wald型置信区间和一个双自举的统计推断方案。我们通过模拟研究证明了所提出方法的性能，并应用这些方法来估计Covid-19治疗试验中协变量调整的效率增益。
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引用次数: 0
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