A simplified formula for quantification of the probability of deterministic assignments in permuted block randomization [0.03%]
分块随机化中确定性分配概率的简化计算公式
Wenle Zhao,Yanqiu Weng
Wenle Zhao
Open label and single blinded randomized controlled clinical trials are vulnerable to selection bias when the next treatment assignment is predictable based on the randomization algorithm and the preceding assignment history. While treatmen...
Grace Wahba
Grace Wahba
We summarize, review and comment upon three papers which discuss the use of discrete, noisy, incomplete, scattered pairwise dissimilarity data in statistical model building. Convex cone optimization codes are used to embed the objects into ...
Peter Müller,Fernando Quintana
Peter Müller
Many recent applications of nonparametric Bayesian inference use random partition models, i.e. probability models for clustering a set of experimental units. We review the popular basic constructions. We then focus on an interesting extensi...
Justin Hall Shows,Wenbin Lu,Hao Helen Zhang
Justin Hall Shows
Censored median regression has proved useful for analyzing survival data in complicated situations, say, when the variance is heteroscedastic or the data contain outliers. In this paper, we study the sparse estimation for censored median re...
Principal Point Classification: Applications to Differentiating Drug and Placebo Responses in Longitudinal Studies [0.03%]
主要点分类在纵向研究中区分药物和安慰剂反应的应用
Thaddeus Tarpey,Eva Petkova
Thaddeus Tarpey
Principal points are cluster means for theoretical distributions. A discriminant methodology based on principal points is introduced. The principal point classification method is useful in clinical trials where the goal is to distinguish an...
Xiao-Feng Wang,Deping Ye
Xiao-Feng Wang
Multivariate local regression is an important tool for image processing and analysis. In many practical biomedical problems, one is often interested in comparing a group of images or regression surfaces. In this paper, we extend the existin...
Bounding the Resampling Risk for Sequential Monte Carlo Implementation of Hypothesis Tests [0.03%]
序贯蒙特卡罗实现假设检验中的重采样风险分析上限研究
Hyune-Ju Kim
Hyune-Ju Kim
Sequential designs can be used to save computation time in implementing Monte Carlo hypothesis tests. The motivation is to stop resampling if the early resamples provide enough information on the significance of the p-value of the original ...
A Self-consistency Approach to Multinomial Logit Model with Random Effects [0.03%]
一种随机效应 multinomial logit 模型的自洽性方法
Shufang Wang,Alex Tsodikov
Shufang Wang
The computation in the multinomial logit mixed effects model is costly especially when the response variable has a large number of categories, since it involves high-dimensional integration and maximization. Tsodikov and Chefo (2008) develo...
Estimation and Efficiency with Recurrent Event Data under Informative Monitoring [0.03%]
在监测信息素影响下以复发性事件数据进行估计和效率分析
Akim Adekpedjou,Edsel A Peña,Jonathan Quiton
Akim Adekpedjou
This article deals with studies that monitor occurrences of a recurrent event for n subjects or experimental units. It is assumed that the i(th) unit is monitored over a random period [0,tau(i)]. The successive inter-event times T(i1), T(i2...
Analytic Bounds on Causal Risk Differences in Directed Acyclic Graphs Involving Three Observed Binary Variables [0.03%]
三元二值观察变量有向无环图中的因果风险差异的界值分析
Sol Kaufman,Jay S Kaufman,Richard F Maclehose
Sol Kaufman
We apply a linear programming approach which uses the causal risk difference (RD(C)) as the objective function and provides minimum and maximum values that RD(C) can achieve under any set of linear constraints on the potential response type...