Kernel Estimation of Bivariate Time-varying Coefficient Model for Longitudinal Data with Terminal Event [0.03%]
纵向数据终端事件下二元时间变化系数模型的核估计方法研究
Yue Wang,Bin Nan,John D Kalbfleisch
Yue Wang
We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectorie...
Variable Selection for High-dimensional Nodal Attributes in Social Networks with Degree Heterogeneity [0.03%]
具有度异质性的社会网络中高维节点属性的选择变量问题
Jia Wang,Xizhen Cai,Xiaoyue Niu et al.
Jia Wang et al.
We consider a class of network models, in which the connection probability depends on ultrahigh-dimensional nodal covariates (homophily) and node-specific popularity (degree heterogeneity). A Bayesian method is proposed to select nodal feat...
Inference for treatment-specific survival curves using machine learning [0.03%]
利用机器学习进行推断以得出特定疗法的生存率曲线
Ted Westling,Alex Luedtke,Peter B Gilbert et al.
Ted Westling et al.
In the absence of data from a randomized trial, researchers may aim to use observational data to draw causal inference about the effect of a treatment on a time-to-event outcome. In this context, interest often focuses on the treatment-spec...
Anna Menacher,Thomas E Nichols,Chris Holmes et al.
Anna Menacher et al.
Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where each voxel represents the existence of a l...
Chunlin Li,Xiaotong Shen,Wei Pan
Chunlin Li
This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive model identifiability under the sublinear growth assumption....
Rungang Han,Pixu Shi,Anru R Zhang
Rungang Han
This paper introduces the functional tensor singular value decomposition (FTSVD), a novel dimension reduction framework for tensors with one functional mode and several tabular modes. The problem is motivated by high-order longitudinal data...
Huiying Mao,Ryan Martin,Brian J Reich
Huiying Mao
Predicting the response at an unobserved location is a fundamental problem in spatial statistics. Given the difficulty in modeling spatial dependence, especially in non-stationary cases, model-based prediction intervals are at risk of missp...
Aritra Halder,Sudipto Banerjee,Dipak K Dey
Aritra Halder
Spatial process models are widely used for modeling point-referenced variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insights into the nature of latent dependence within the studied ...
Emily C Hector,Brian J Reich
Emily C Hector
Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using max stable processes (MSPs) that are computationally prohibitive to fit for as few as a dozen observations. Supposed computa...
Higher-Order Least Squares: Assessing Partial Goodness of Fit of Linear Causal Models [0.03%]
高阶最小二乘法：线性因果模型部分拟合优度的评估
Christoph Schultheiss,Peter Bühlmann,Ming Yuan
Christoph Schultheiss
We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular, we consider situations where hidden confounding is potential...