551期

最新文章

• Estimating Heterogeneous Exposure Effects in the Case-Crossover Design Using BART AI翻译
  作者：Englert, Jacob R.; Ebelt, Stefanie T.; Chang, Howard H.
  作者单位：Emory University; Rollins School Public Health; Emory University
  摘要：Epidemiological approaches for examining human health responses to environmental exposures in observational studies often control for confounding by implementing clever matching schemes and using statistical methods based on conditional likelihood. Nonparametric regression models have surged in popularity in recent years as a tool for estimating individual-level heterogeneous effects, which provide a more detailed picture of the exposure-response relationship but can also be aggregated to obta...
• Distributional Outcome Regression via Quantile Functions and its Application to Modelling Continuously Monitored Heart Rate and Physical Activity AI翻译
  作者：Ghosal, Rahul; Ghosh, Sujit K.; Schrack, Jennifer A.; Zipunnikov, Vadim
  作者单位：University of South Carolina System; University of South Carolina Columbia; North Carolina State University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：Modern clinical and epidemiological studies widely employ wearables to record parallel streams of real-time data on human physiology and behavior. With recent advances in distributional data analysis, these high-frequency data are now often treated as distributional observations resulting in novel regression settings. Motivated by these modeling setups, we develop a distributional outcome regression via quantile functions (DORQF) that expands existing literature with three key contributions: (...
• Robust Inference for Federated Meta-Learning AI翻译
  作者：Guo, Zijian; Li, Xiudi; Han, Larry; Cai, Tianxi
  作者单位：Rutgers University System; Rutgers University New Brunswick; University of California System; University of California Berkeley; Northeastern University; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School
  摘要：Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogeneity across sources and data-sharing constraints. In this article, we consider a general robust inference framework for federated meta-learning of data from multiple sites, enabling statistical inference for the prevailing model, defined as the one matching the majority of the sites. Statistical inference for the prev...
• Conformal Prediction for Network-Assisted Regression AI翻译
  作者：Lunde, Robert; Levina, Elizaveta; Zhu, Ji
  作者单位：Washington University (WUSTL); University of Michigan System; University of Michigan
  摘要：An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventional node covariates, such as demographic characteristics. While standard regression methods that make use of both types of covariates may be used for prediction, statistical inference is complicated by the fact that the nodal summary statistics are often dependent in complex ways. We show that under a mild joint exchan...
• High-Dimensional Expected Shortfall Regression AI翻译
  作者：Zhang, Shushu; He, Xuming; Tan, Kean Ming; Zhou, Wen-Xin
  作者单位：University of Michigan System; University of Michigan; Washington University (WUSTL); University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：Expected shortfall is defined as the average over the tail below (or above) a certain quantile of a probability distribution. Expected shortfall regression provides powerful tools for learning the relationship between a response variable and a set of covariates while exploring the heterogeneous effects of the covariates. In the health disparity research, for example, the lower/upper tail of the conditional distribution of a health-related outcome, given high-dimensional covariates, is often of...
• Phase-Type Distributions for Sieve Estimation AI翻译
  作者：Hu, Xiangbin; Wang, Yudong; Ye, Zhisheng; Zhao, Xingqiu
  作者单位：National University of Singapore; Hong Kong Polytechnic University
  摘要：In many semiparametric models, the infinite-dimensional parameter of direct interest is a probability density, but its nonparametric estimation is usually difficult in the presence of incomplete data. To address this issue, this study promotes phase-type distributions as a method of sieve. Phase-type distributions are dense in the space of nonnegative distributions, closed under minimum, maximum, and convolution, and compatible with the accelerated failure time model. This renders them attract...
• Distributional Off-Policy Evaluation in Reinforcement Learning AI翻译
  作者：Qi, Zhengling; Bai, Chenjia; Wang, Zhaoran; Wang, Lan
  作者单位：George Washington University; Harbin Institute of Technology; Northwestern University; University of Miami; China Telecom Corp. Ltd.
  摘要：In the literature of reinforcement learning (RL), off-policy evaluation is mainly focused on estimating a value of a target policy given the pre-collected data generated by some behavior policy. Motivated by the recent success of distributional RL in many practical applications, we study the distributional off-policy evaluation problem in the batch setting when the reward is multi-variate. We propose an offline Wasserstein-based approach to simultaneously estimate the joint distribution of a m...
• Estimating Heterogeneous Causal Mediation Effects with Bayesian Decision Tree Ensembles AI翻译
  作者：Ting, Angela; Linero, Antonio R.
  作者单位：University of Texas System; University of Texas Austin
  摘要：The causal inference literature has increasingly recognized that targeting treatment effect heterogeneity can lead to improved scientific understanding and policy recommendations. Similarly, studying the causal pathway connecting the treatment to the outcome can be useful. We address these problems in the context of causal mediation analysis. We introduce a varying coefficient model based on Bayesian additive regression trees to estimate and regularize heterogeneous causal mediation effects. E...
• Partially Exchangeable Stochastic Block Models for (Node-Colored) Multilayer Networks AI翻译
  作者：Gaffi, Francesco; Durante, Daniele; Lijoi, Antonio; Prunster, Igor
  作者单位：Bocconi University; University System of Maryland; University of Maryland College Park
  摘要：Multilayer networks generalize single-layered connectivity data in several directions. These generalizations include, among others, settings where multiple types of edges are observed among the same set of nodes (edge-colored networks) or where a single notion of connectivity is measured between nodes belonging to different pre-specified layers (node-colored networks). While progress has been made in statistical modeling of edge-colored networks, principled approaches that flexibly account for...
• Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment AI翻译
  作者：Ben-Michael, Eli; Greiner, D. James; Imai, Kosuke; Jiang, Zhichao
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Harvard University; Harvard University; Harvard University; Sun Yat Sen University
  摘要：Algorithmic recommendations and decisions have become ubiquitous in today's society. Many of these data-driven policies, especially in the realm of public policy, are based on known, deterministic rules to ensure their transparency and interpretability. We examine a particular case of algorithmic pre-trial risk assessments in the US criminal justice system, which provide deterministic classification scores and recommendations to help judges make release decisions. Our goal is to analyze data f...