Testing and Confidence Intervals for High Dimensional Proportional Hazards Model [0.03%]
高维比例 Hazard 模型的检验和置信区间
Ethan X Fang,Yang Ning,Han Liu
Ethan X Fang
This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models. Motivated by the geometric projection principle, we ...
Testing for the Markov property in time series via deep conditional generative learning [0.03%]
基于深度条件生成学习的时间序列Markov性质检验
Yunzhe Zhou,Chengchun Shi,Lexin Li et al.
Yunzhe Zhou et al.
The Markov property is widely imposed in analysis of time series data. Correspondingly, testing the Markov property, and relatedly, inferring the order of a Markov model, are of paramount importance. In this article, we propose a nonparamet...
Estimating the Efficiency Gain of Covariate-Adjusted Analyses in Future Clinical Trials Using External Data [0.03%]
利用外部数据估计未来临床试验中调整协变量分析的效率提升功效
Xiudi Li,Sijia Li,Alex Luedtke
Xiudi Li
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 ef...
Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution [0.03%]
非识别联合分布下的非 ignorable 缺失数据的均值函数的非参数统计推断
Wei Li,Wang Miao,Eric Tchetgen Tchetgen
Wei Li
We consider identification and inference about mean functionals of observed covariates and an outcome variable subject to non-ignorable missingness. By leveraging a shadow variable, we establish a necessary and sufficient condition for iden...
[This corrects the article DOI: 10.1093/jrsssb/qkad051.]. © The Royal Statistical Society 2023.
Changbo Zhu,Jane-Ling Wang
Changbo Zhu
Testing the homogeneity between two samples of functional data is an important task. While this is feasible for intensely measured functional data, we explain why it is challenging for sparsely measured functional data and show what can be ...
Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation [0.03%]
基于随机临床试验和真实世界数据的异质性弹性整合分析方法研究
Shu Yang,Chenyin Gao,Donglin Zeng et al.
Shu Yang et al.
We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our appro...
Changbo Zhu,Hans-Georg Müller
Changbo Zhu
Series of univariate distributions indexed by equally spaced time points are ubiquitous in applications and their analysis constitutes one of the challenges of the emerging field of distributional data analysis. To quantify such distributio...
A simple new approach to variable selection in regression, with application to genetic fine mapping [0.03%]
一种新的回归变量选择方法及其在遗传精细定位中的应用
Gao Wang,Abhishek Sarkar,Peter Carbonetto et al.
Gao Wang et al.
We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model - the "Sum of Single Effects" (SuS...
High-dimensional principal component analysis with heterogeneous missingness [0.03%]
具有异构缺失性的高维主成分分析
Ziwei Zhu,Tengyao Wang,Richard J Samworth
Ziwei Zhu
We study the problem of high-dimensional Principal Component Analysis (PCA) with missing observations. In a simple, homogeneous observation model, we show that an existing observed-proportion weighted (OPW) estimator of the leading principa...