553期

最新文章

• Design-Based Uncertainty for Quasi-Experiments AI翻译
  作者：Rambachan, Ashesh; Roth, Jonathan
  作者单位：Massachusetts Institute of Technology (MIT); Brown University
  摘要：Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This article develops a design-based framework suitable for analyzing quasi-experimental settings in the social sciences, in which the treatment assignment can be viewed as the realization of some stochastic process but there is concern about unobserved selection into treatment. In our framework, treatments are stochastic, but units may differ in their probabilities ...
• Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment AI翻译
  作者：Heng, Siyu; Zhang, Jiawei; Feng, Yang
  作者单位：New York University; New York University; University of Chicago; New York University
  摘要：Design-based causal inference, also known as randomization-based or finite-population causal inference, is one of the most widely used causal inference frameworks, largely due to the merit that its validity can be guaranteed by study design (e.g., randomized experiments) and does not require assuming specific outcome-generating distributions or super-population models. Despite its advantages, design-based causal inference can still suffer from other issues, among which outcome missingness is a...
• A Practical Interval Estimation Method for Spectral Density Function AI翻译
  作者：Yu, Haihan; Kaiser, Mark S.; Nordman, Daniel J.
  作者单位：University of Rhode Island; Iowa State University
  摘要：The spectral density function can play a key role in time series analysis, where nonparametric interval estimation of the spectral density is a fundamental issue. However, the prevailing pointwise interval methods for spectral densities, including Chi-square approximation and frequency domain bootstrap (FDB), can be misleading in practice, perhaps more so than appreciated, as confidence intervals often exhibit low coverage accuracy as well as high sensitivity to tuning parameters. To provide a...
• Rejoinder to the Discussions on LAMBDA: A Large Model Based Data Agent AI翻译
  作者：Sun, Maojun; Han, Ruijian; Jiang, Binyan; Qi, Houduo; Sun, Defeng; Yuan, Yancheng; Huang, Jian
  作者单位：Hong Kong Polytechnic University; Hong Kong Polytechnic University
• Discussion of LAMBDA: A Large Model Based Data Agent AI翻译
  作者：Donoho, David
  作者单位：Stanford University
• Online Policy Learning and Inference by Matrix Completion AI翻译
  作者：Duan, Congyuan; Li, Jingyang; Xia, Dong
  作者单位：Hong Kong University of Science & Technology; University of Michigan System; University of Michigan
  摘要：Is it possible to make online decisions when personalized covariates are unavailable? We take a collaborative-filtering approach for decision-making based on collective preferences. By assuming low-dimensional latent features, we formulate the covariate-free decision-making problem as a matrix completion bandit. We propose a policy learning procedure that combines an epsilon -greedy policy for decision-making with an online gradient descent algorithm for bandit parameter estimation. Our novel ...
• Higher-Order Accurate Two-Sample Network Inference and Network Hashing AI翻译
  作者：Shao, Meijia; Xia, Dong; Zhang, Yuan; Wu, Qiong; Chen, Shuo
  作者单位：University System of Ohio; Ohio State University; Hong Kong University of Science & Technology; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University System of Maryland; University of Maryland Baltimore
  摘要：Two-sample hypothesis testing for network comparison presents many significant challenges, including: leveraging repeated network observations and known node registration, but without requiring them to operate; relaxing strong structural assumptions; achieving finite-sample higher-order accuracy; handling different network sizes and sparsity levels; fast computation and memory parsimony; controlling false discovery rate (FDR) in multiple testing; and theoretical understandings, particularly re...
• Checking the Cox Proportional Hazards Model with Interval-Censored Data AI翻译
  作者：Xu, Yangjianchen; Zeng, Donglin; Lin, D. Y.
  作者单位：University of Waterloo; University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill
  摘要：This article presents a general framework for checking the adequacy of the Cox proportional hazards model with interval-censored data, which arise when the event of interest is known only to occur over a random time interval. Specifically, we construct certain stochastic processes that are informative about various aspects of the model, that is, the functional forms of covariates, the exponential link function and the proportional hazards assumption. We establish their weak convergence to zero...
• Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures AI翻译
  作者：Wang, Zeya; Ye, Chenglong
  作者单位：University of Kentucky
  摘要：Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensional embeddings before partitioning, which embarks unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering for two reasons: (a) the curse of dimensionality when applied to the high-dimensional input data, and (b) unreliable comparison of clustering results when...
• Likelihood Methods in Survival Analysis: With R Examples AI翻译
  作者：Mao, Lu
  作者单位：University of Wisconsin System; University of Wisconsin Madison