2023

最新文章

• Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension AI翻译
  作者：Wu, Yunan; Wang, Lan; Fu, Haoda
  作者单位：University of Texas System; University of Texas Dallas; University of Miami; Eli Lilly
  摘要：This article develops new tools to quantify uncertainty in optimal decision making and to gain insight into which variables one should collect information about given the potential cost of measuring a large number of variables. We investigate simultaneous inference to determine if a group of variables is relevant for estimating an optimal decision rule in a high-dimensional semiparametric framework. The unknown link function permits flexible modeling of the interactions between the treatment a...
• Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation AI翻译
  作者：Deb, Nabarun; Sen, Bodhisattva
  作者单位：Columbia University
  摘要：In this article, we propose a general framework for distribution-free nonparametric testing in multi-dimensions, based on a notion of multivariate ranks defined using the theory of measure transportation. Unlike other existing proposals in the literature, these multivariate ranks share a number of useful properties with the usual one-dimensional ranks; most importantly, these ranks are distribution-free. This crucial observation allows us to design nonparametric tests that are exactly distribu...
• Accommodating Time-Varying Heterogeneity in Risk Estimation under the Cox Model: A Transfer Learning Approach AI翻译
  作者：Li, Ziyi; Shen, Yu; Ning, Jing
  作者单位：University of Texas System; UTMD Anderson Cancer Center
  摘要：Transfer learning has attracted increasing attention in recent years for adaptively borrowing information across different data cohorts in various settings. Cancer registries have been widely used in clinical research because of their easy accessibility and large sample size. Our method is motivated by the question of how to use cancer registry data as a complement to improve the estimation precision of individual risks of death for inflammatory breast cancer (IBC) patients at The University o...
• Metropolis-Hastings via Classification AI翻译
  作者：Kaji, Tetsuya; Rockova, Veronika
  作者单位：University of Chicago
  摘要：This article develops a Bayesian computational platform at the interface between posterior sampling and optimization in models whose marginal likelihoods are difficult to evaluate. Inspired by contrastive learning and Generative Adversarial Networks (GAN), we reframe the likelihood function estimation problem as a classification problem. Pitting a Generator, who simulates fake data, against a Classifier, who tries to distinguish them from the real data, one obtains likelihood (ratio) estimator...
• Fairness-Oriented Learning for Optimal Individualized Treatment Rules AI翻译
  作者：Fang, Ethan X.; Wang, Zhaoran; Wang, Lan
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Northwestern University; University of Miami; Duke University
  摘要：There has recently been a surge on the methodological development for optimal individualized treatment rule (ITR) estimation. The standard methods in the literature are designed to maximize the potential average performance (assuming larger outcomes are desirable). A notable drawback of the standard approach, due to heterogeneity in treatment response, is that the estimated optimal ITR may be suboptimal or even detrimental to certain disadvantaged subpopulations. Motivated by the importance of...
• Is a Classification Procedure Good Enough?-A Goodness-of-Fit Assessment Tool for Classification Learning AI翻译
  作者：Zhang, Jiawei; Ding, Jie; Yang, Yuhong
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：In recent years, many nontraditional classification methods, such as random forest, boosting, and neural network, have been widely used in applications. Their performance is typically measured in terms of classification accuracy. While the classification error rate and the like are important, they do not address a fundamental question: Is the classification method underfitted? To our best knowledge, there is no existing method that can assess the goodness of fit of a general classification pro...
• A Kernel Log-Rank Test of Independence for Right-Censored Data AI翻译
  作者：Fernandez, Tamara; Gretton, Arthur; Rindt, David; Sejdinovic, Dino
  作者单位：University of London; University College London; Universidad Adolfo Ibanez; University of Oxford
  摘要：We introduce a general nonparametric independence test between right-censored survival times and covariates, which may be multivariate. Our test statistic has a dual interpretation, first in terms of the supremum of a potentially infinite collection of weight-indexed log-rank tests, with weight functions belonging to a reproducing kernel Hilbert space (RKHS) of functions; and second, as the norm of the difference of embeddings of certain finite measures into the RKHS, similar to the Hilbert-Sc...
• Functional Time Series Prediction Under Partial Observation of the Future Curve AI翻译
  作者：Jiao, Shuhao; Aue, Alexander; Ombao, Hernando
  作者单位：King Abdullah University of Science & Technology; University of California System; University of California Davis
  摘要：This article tackles one of the most fundamental goals in functional time series analysis which is to provide reliable predictions for future functions. Existing methods for predicting a complete future functional observation use only completely observed trajectories. We develop a new method, called partial functional prediction (PFP), which uses both completely observed trajectories and partial information (available partial data) on the trajectory to be predicted. The PFP method includes an ...
• Conditional Functional Graphical Models AI翻译
  作者：Lee, Kuang-Yao; Ji, Dingjue; Li, Lexin; Constable, Todd; Zhao, Hongyu
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Yale University; University of California System; University of California Berkeley; Yale University
  摘要：Graphical modeling of multivariate functional data is becoming increasingly important in a wide variety of applications. The changes of graph structure can often be attributed to external variables, such as the diagnosis status or time, the latter of which gives rise to the problem of dynamic graphical modeling. Most existing methods focus on estimating the graph by aggregating samples, but largely ignore the subject-level heterogeneity due to the external variables. In this article, we introd...
• Regression Discontinuity Designs With a Continuous Treatment AI翻译
  作者：Dong, Yingying; Lee, Ying-Ying; Gou, Michael
  作者单位：University of California System; University of California Irvine
  摘要：The standard regression discontinuity (RD) design deals with a binary treatment. Many empirical applications of RD designs involve continuous treatments. This article establishes identification and robust bias-corrected inference for such RD designs. Causal identification is achieved by using any changes in the distribution of the continuous treatment at the RD threshold (including the usual mean change as a special case). We discuss a double-robust identification approach and propose an estim...