William Lippitt,Nichole E Carlson,Jaron Arbet et al.
William Lippitt et al.
It is now common to have a modest to large number of features on individuals with complex diseases. Unsupervised analyses, such as clustering with and without preprocessing by Principle Component Analysis (PCA), is widely used in practice t...
Linhan Hu,Soutrik Mandal,Samiran Sinha
Linhan Hu
Interval-censored data are ubiquitous in clinical studies where actual time-to-event is difficult to measure. A number of nonparametric tests have been proposed to conduct a two-sample test using interval-censored data, and these tests can ...
Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies [0.03%]
缺失数据多重插补中带跳转模式的协变量的比较研究
Guangyu Zhang,Yulei He,Bill Cai et al.
Guangyu Zhang et al.
Multiple imputation (MI) is a widely used approach to address missing data issues in surveys. Variables included in MI can have various distributional forms with different degrees of missingness. However, when variables with missing data co...
Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates [0.03%]
考虑协变量条件下的最优顺序设计的非近视和伪非近视方法
Mia Sato Tackney,Dave Woods,Ilya Shpitser
Mia Sato Tackney
In sequential experiments, subjects become available for the study over a period of time, and covariates are often measured at the time of arrival. We consider the setting where the sample size is fixed but covariate values are unknown unti...
Sehwan Kim,Qifan Song,Faming Liang
Sehwan Kim
We propose a class of adaptive stochastic gradient Markov chain Monte Carlo (SGMCMC) algorithms, where the drift function is adaptively adjusted according to the gradient of past samples to accelerate the convergence of the algorithm in sim...
Bayesian auxiliary variable model for birth records data with qualitative and quantitative responses [0.03%]
具有定性和定量反应的出生记录数据的贝叶斯辅助变量模型
Xiaoning Kang,Shyam Ranganathan,Lulu Kang et al.
Xiaoning Kang et al.
Many applications involve data with qualitative and quantitative responses. When there is an association between the two responses, a joint model will produce improved results than fitting them separately. In this paper, a Bayesian method i...
Using the potential outcome framework to estimate optimal sample size for cluster randomized trials: a simulation-based algorithm [0.03%]
利用潜在结果框架来估计集群随机试验的最优样本量：一种基于模拟的算法
Ruoshui Zhai,Roee Gutman
Ruoshui Zhai
In cluster randomized trials (CRTs) groups rather than individuals are randomized to different interventions. Individuals' responses within clusters are commonly more similar than those across clusters. This dependency introduces complexity...
Smooth and Locally Sparse Estimation for Multiple-Output Functional Linear Regression [0.03%]
多输出函数线性回归的光滑且局部稀疏估计方法研究
Kuangnan Fang,Xiaochen Zhang,Shuangge Ma et al.
Kuangnan Fang et al.
Functional data analysis has attracted substantial research interest and the goal of functional sparsity is to produce a sparse estimate which assigns zero values over regions where the true underlying function is zero, i.e., no relationshi...
Robert H Lyles,Paul Weiss,Lance A Waller
Robert H Lyles
Drawbacks of traditional approximate (Wald test-based) and exact (Clopper-Pearson) confidence intervals for a binomial proportion are well-recognized. Alternatives include an interval based on inverting the score test, adaptations of exact ...
Shengtong Han,Hongmei Zhang,Wenhui Sheng et al.
Shengtong Han et al.
This article focuses on the clustering problem based on Dirichlet process (DP) mixtures. To model both time invariant and temporal patterns, different from other existing clustering methods, the proposed semi-parametric model is flexible in...