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...
Yudong Chen,Tengyao Wang,Richard J Samworth
Yudong Chen
We introduce and study two new inferential challenges associated with the sequential detection of change in a high-dimensional mean vector. First, we seek a confidence interval for the changepoint, and second, we estimate the set of indices...
Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer [0.03%]
基于知识迁移的高维广义线性模型的估计和推断
Sai Li,Linjun Zhang,T Tony Cai et al.
Sai Li et al.
Transfer learning provides a powerful tool for incorporating data from related studies into a target study of interest. In epidemiology and medical studies, the classification of a target disease could borrow information across other relate...
Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution [0.03%]
用于估计空气污染暴露对个人不良妊娠结局影响的异质性分布式滞后模型
Daniel Mork,Marianthi-Anna Kioumourtzoglou,Marc Weisskopf et al.
Daniel Mork et al.
Children's health studies support an association between maternal environmental exposures and children's birth outcomes. A common goal is to identify critical windows of susceptibility-periods during gestation with increased association bet...
Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis [0.03%]
基于系数阈值的稳健高维回归及其在影像数据分析中的应用
Bingyuan Liu,Qi Zhang,Lingzhou Xue et al.
Bingyuan Liu et al.
It is important to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible heavy tails and outliers in real-world applications such as imaging data analyses. We propose a new r...
Elizabeth L Ogburn,Oleg Sofrygin,Iván Díaz et al.
Elizabeth L Ogburn et al.
We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other unit...
Optimal Nonparametric Inference with Two-Scale Distributional Nearest Neighbors [0.03%]
双尺度分布最近邻的最优非参数推断方法研究
Emre Demirkaya,Yingying Fan,Lan Gao et al.
Emre Demirkaya et al.
The weighted nearest neighbors (WNN) estimator has been popularly used as a flexible and easy-to-implement nonparametric tool for mean regression estimation. The bagging technique is an elegant way to form WNN estimators with weights automa...
Scaled Process Priors for Bayesian Nonparametric Estimation of the Unseen Genetic Variation [0.03%]
用于估计未见遗传变异的贝叶斯非参数方法中的Scaled Process先验研究
Federico Camerlenghi,Stefano Favaro,Lorenzo Masoero et al.
Federico Camerlenghi et al.
There is a growing interest in the estimation of the number of unseen features, mostly driven by biological applications. A recent work brought out a peculiar property of the popular completely random measures (CRMs) as prior models in Baye...
A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders [0.03%]
一种半参数逆强化学习方法，用于表征精神障碍的决策过程
Xingche Guo,Donglin Zeng,Yuanjia Wang
Xingche Guo
Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years. Emerging evidence indicates the presence of reward processing abnormalities in MDD. An important scientific question is whether the abnormalitie...