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...
Xiaoxi Shen,Chang Jiang,Lyudamila Sakhanenko et al.
Xiaoxi Shen et al.
Neural networks have become one of the most popularly used methods in machine learning and artificial intelligence. Due to the universal approximation theorem (Hornik et al., 1989), a neural network with one hidden layer can approximate any...
Permutation-Based Inference for Function-on-Scalar Regression With an Application in PET Brain Imaging [0.03%]
基于排列的函数回归推论及其在脑PET成像中的应用
Denise Shieh,R Todd Ogden
Denise Shieh
The density of various proteins throughout the human brain can be studied through the use of positron emission tomography (PET) imaging. We report here on data from a study of serotonin transporter (5-HTT) binding. While PET imaging data an...
A Semiparametric Bayesian Approach to Epidemics, with Application to the Spread of the Coronavirus MERS in South Korea in 2015 [0.03%]
半参数贝叶斯流行病传播分析方法及中东呼吸综合征疫情实证研究
Michael Schweinberger,Rashmi P Bomiriya,Sergii Babkin
Michael Schweinberger
We consider incomplete observations of stochastic processes governing the spread of infectious diseases through finite populations by way of contact. We propose a flexible semiparametric modeling framework with at least three advantages. Fi...