Yin Tang,Yanyuan Ma,Bing Li
Yin Tang
We conduct a KL-divergence based procedure for testing elliptical distributions. The procedure simultaneously takes into account the two defining properties of an elliptically distributed random vector: independence between length and direc...
Soft Bayesian Additive Regression Trees (SBART) for correlated survey response with non-Gaussian error [0.03%]
具有非高斯误差的关联调查响应的软贝叶斯加法回归树（SBART）
Abhishek Mandal,Antonio R Linero,Dipankar Bandyopadhyay et al.
Abhishek Mandal et al.
Complex surveys are important data resources across diverse domains, including social sciences, public health, and market research. When faced with unknown effects and interactions of multiple covariates, analysis using usual parametric reg...
A comparison of causal inference methods for evaluating multiple treatment groups [0.03%]
多种处理组的因果推断方法比较研究
Shuai Chen,Hao Wu,Hongwei Zhao
Shuai Chen
Causal inference is formulated using the counterfactual framework, enabling direct investigation of causal questions. Causal inference methods can incorporate machine learning techniques into the estimation process, allowing for more flexib...
Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates [0.03%]
稀疏纵向协变量的乘法风险模型的时间依赖系数的回归分析
Zhuowei Sun,Hongyuan Cao
Zhuowei Sun
We study the multiplicative hazards model with intermittently observed longitudinal covariates and time-varying coefficients. For such models, the existing ad hoc approach, such as the last value carried forward, is biased. We propose a ker...
TSSS: A Novel Triangulated Spherical Spline Smoothing for Surface-Based Data [0.03%]
基于表面数据的新型三角球面样条平滑方法(TSSS)
Zhiling Gu,Shan Yu,Guannan Wang et al.
Zhiling Gu et al.
Surface-based data are prevalent across diverse practical applications in various fields. This paper introduces a novel nonparametric method to discover the underlying signals from data distributed on complex surface-based domains. The prop...
Nonparametric Density Estimation for Data Scattered on Irregular Spatial Domains: A Likelihood-Based Approach Using Bivariate Penalized Spline Smoothing [0.03%]
基于 likelihood 的方法使用二元惩罚样条平滑估计不规则区域上的数据集的非参数密度估计方法
Kunal Das,Shan Yu,Guannan Wang et al.
Kunal Das et al.
Accurately estimating data density is crucial for making informed decisions and modeling in various fields. This paper presents a novel nonparametric density estimation procedure that utilizes bivariate penalized spline smoothing over trian...
Avoiding the Surrogate Paradox: An Empirical Framework for Assessing Assumptions [0.03%]
避免代理悖论：评估假设的实证框架
Emily Hsiao,Lu Tian,Layla Parast
Emily Hsiao
The use of surrogate markers to replace a primary outcome in clinical trials has the potential to allow earlier decisions about the effectiveness of a treatment when a direct measurement of the primary outcome is difficult to obtain. Howeve...
Bingyuan Liu,Lingzhou Xue
Bingyuan Liu
The sufficient dimension reduction (SDR) with sparsity has received much attention for analysing high-dimensional data. We study a nonparametric sparse kernel sufficient dimension reduction (KSDR) based on the reproducing kernel Hilbert spa...
Enhanced doubly robust estimation with concave link functions for estimands in clinical trials [0.03%]
临床试验中具有凹链接函数的增强双重稳健估计
Junyi Zhang,Ao Yuan,Ming T Tan
Junyi Zhang
For observational studies or clinical trials not fully randomized, the baseline covariates are often not balanced between the treatment and control groups. In this case, the traditional estimates of treatment effects are biased, and causal ...
A Simple Nonparametric Least-Squares-Based Causal Inference for Heterogeneous Treatment Effects [0.03%]
一种简单的非参式异质性治疗效应因果推断方法
Ying Zhang,Yuanfang Xu,Bristol Myers Squibb et al.
Ying Zhang et al.
Estimating treatment effects is a common practice in making causal inferences. However, it is a challenging task for observational studies because the underlying models for outcome and treatment assignment are unknown. The concept of potent...