Jianqing Fan,Han Liu,Weichen Wang et al.
Jianqing Fan et al.
Heterogeneity is an unwanted variation when analyzing aggregated datasets from multiple sources. Though different methods have been proposed for heterogeneity adjustment, no systematic theory exists to justify these methods. In this work, w...
Robustifying Trial-Derived Optimal Treatment Rules for A Target Population [0.03%]
鲁棒化由临床试验派生的针对目标人群的最优治疗规则
Ying-Qi Zhao,Donglin Zeng,Catherine M Tangen et al.
Ying-Qi Zhao et al.
Treatment rules based on individual patient characteristics that are easy to interpret and disseminate are important in clinical practice. Properly planned and conducted randomized clinical trials are used to construct individualized treatm...
Betsabé G Blas Achic,Tianying Wang,Ya Su et al.
Betsabé G Blas Achic et al.
Epidemiologists often categorize a continuous risk predictor, even when the true risk model is not a categorical one. Nonetheless, such categorization is thought to be more robust and interpretable, and thus their goal is to fit the categor...
Empirical likelihood based tests for stochastic ordering under right censorship [0.03%]
基于经验似然的右 censorship下的随机序检验方法研究
Hsin-Wen Chang,Ian W McKeague
Hsin-Wen Chang
This paper develops an empirical likelihood approach to testing for stochastic ordering between two univariate distributions under right censorship. The proposed test is based on a maximally selected local empirical likelihood statistic. Th...
Exchangeable Markov survival processes and weak continuity of predictive distributions [0.03%]
可交换的马尔可夫生存过程和预测分布的弱连续性
Walter Dempsey,Peter McCullagh
Walter Dempsey
We study exchangeable, Markov survival processes - stochastic processes giving rise to infinitely exchangeable non-negative sequences (T 1, T 2, …). We show how these are determined by their characteristic index { ζ n } n = 1 ∞...
X Jessie Jeng,Wenbin Lu,Huimin Peng
X Jessie Jeng
Recent development in statistical methodology for personalized treatment decision has utilized high-dimensional regression to take into account a large number of patients' covariates and described personalized treatment decision through int...
Dimension reduction and estimation in the secondary analysis of case-control studies [0.03%]
病例对照研究的二次分析中的降维与估计问题
Liang Liang,Raymond Carroll,Yanyuan Ma
Liang Liang
Studying the relationship between covariates based on retrospective data is the main purpose of secondary analysis, an area of increasing interest. We examine the secondary analysis problem when multiple covariates are available, while only...
Statistical properties of simple random-effects models for genetic heritability [0.03%]
遗传性状随机效应模型的统计性质分析
David Steinsaltz,Andrew Dahl,Kenneth W Wachter
David Steinsaltz
Random-effects models are a popular tool for analysing total narrow-sense heritability for quantitative phenotypes, on the basis of large-scale SNP data. Recently, there have been disputes over the validity of conclusions that may be drawn ...
Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures [0.03%]
基于狄利克雷过程混合的可扩展贝叶斯非参数探索成对依赖性措施
Sarah Filippi,Chris C Holmes,Luis E Nieto-Barajas
Sarah Filippi
In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distribut...
Eric F Lock,Gen Li
Eric F Lock
We describe a probabilistic PARAFAC/CANDECOMP (CP) factorization for multiway (i.e., tensor) data that incorporates auxiliary covariates, SupCP. SupCP generalizes the supervised singular value decomposition (SupSVD) for vector-valued observ...