Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects [0.03%]
因果中介分析：从简单的到更稳健的估计边际自然(介导/直接)效应策略
Trang Quynh Nguyen,Elizabeth L Ogburn,Ian Schmid et al.
Trang Quynh Nguyen et al.
This paper aims to provide practitioners of causal mediation analysis with a better understanding of estimation options. We take as inputs two familiar strategies (weighting and model-based prediction) and a simple way of combining them (we...
Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists [0.03%]
标量与函数回归预测从密集采集的数据中长期结果的变化：应用于科学家的解释性问题
John J Dziak,Donna L Coffman,Matthew Reimherr et al.
John J Dziak et al.
Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a sem...
Bomin Kim,Kevin H Lee,Lingzhou Xue et al.
Bomin Kim et al.
We present a selective review of statistical modeling of dynamic networks. We focus on models with latent variables, specifically, the latent space models and the latent class models (or stochastic blockmodels), which investigate both the o...
Julie Josse,Susan Holmes
Julie Josse
Simple correlation coefficients between two variables have been generalized to measure association between two matrices in many ways. Coefficients such as the RV coefficient, the distance covariance (dCov) coefficient and kernel based coeff...
Adrien Saumard,Jon A Wellner
Adrien Saumard
We review and formulate results concerning log-concavity and strong-log-concavity in both discrete and continuous settings. We show how preservation of log-concavity and strongly log-concavity on ℝ under convolution follows from a fundamen...
Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain*† [0.03%]
融合统计学和网络科学解析复杂脑功能网络以理解大脑*†
Sean L Simpson,F DuBois Bowman,Paul J Laurienti
Sean L Simpson
Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated t...
Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules [0.03%]
威尔科克森秩和检验或t检验？假设检验的假设以及决策规则的多种解读方法
Michael P Fay,Michael A Proschan
Michael P Fay
In a mathematical approach to hypothesis tests, we start with a clearly defined set of hypotheses and choose the test with the best properties for those hypotheses. In practice, we often start with less precise hypotheses. For example, ofte...
Testing polynomial covariate effects in linear and generalized linear mixed models [0.03%]
多项式协变量效应的检验在混合线性模型和广义混合线性模型中的应用
Mingyan Huang,Daowen Zhang
Mingyan Huang
An important feature of linear mixed models and generalized linear mixed models is that the conditional mean of the response given the random effects, after transformed by a link function, is linearly related to the fixed covariate effects ...