2025

最新文章

• α-separability and adjustable combination of amplitude and phase model for functional data AI翻译
  作者：Wang, Tian; Ding, Jimin
  作者单位：Columbia University; Washington University (WUSTL)
  摘要：We consider separating and joint modelling amplitude and phase variations for functional data in an identifiable manner. To rigorously address this separability issue, we introduce the notion of alpha-separability upon constructing a family of alpha-indexed metrics. We bridge alpha-separability with the uniqueness of Fr & eacute;chet mean, leading to the proposed adjustable combination of amplitude and phase model. The parameter alpha allows user-defined modelling emphasis between vertical and...
• Long-term causal inference under persistent confounding via data combination AI翻译
  作者：Imbens, Guido; Kallus, Nathan; Mao, Xiaojie; Wang, Yuhao
  作者单位：Stanford University; Cornell University; Tsinghua University; Tsinghua University; Shanghai Qi Zhi Institute
  摘要：We study the identification and estimation of long-term treatment effects by combining short-term experimental data and long-term observational data subject to unobserved confounding. This problem arises often when concerned with long-term treatment effects since experiments are often short-term due to operational necessity while observational data can be more easily collected over longer time frames but may be subject to confounding. In this paper, we tackle the challenge of persistent confou...
• Root cause discovery via permutations and Cholesky decomposition AI翻译
  作者：Li, Jinzhou; Chu, Benjamin B.; Scheller, Ines F.; Gagneur, Julien; Maathuis, Marloes H.
  作者单位：National University of Singapore; Stanford University; Technical University of Munich; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Technical University of Munich; Technical University of Munich; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：This work is motivated by the following problem: Can we identify the disease-causing gene in a patient affected by a monogenic disorder? This problem is an instance of root cause discovery. Specifically, we aim to identify the intervened variable in one interventional sample using a set of observational samples as reference. We consider a linear structural equation model where the causal ordering is unknown. We begin by examining a simple method that uses squared z-scores and characterize the ...
• Efficient nonparametric estimators of discrimination measures with censored survival data AI翻译
  作者：Breum, Marie Skov; Martinussen, Torben
  作者单位：University of Copenhagen
  摘要：Discrimination measures such as the concordance index and the cumulative-dynamic time-dependent area under the ROC-curve are widely used in the medical literature for evaluating the predictive accuracy of a scoring rule which relates a set of prognostic markers to the risk of experiencing a particular event. Often the scoring rule being evaluated in terms of discriminatory ability is the linear predictor of a survival regression model such as the Cox proportional hazards model. This has the un...
• Robust angle-based transfer learning in high dimensions AI翻译
  作者：Gu, Tian; Han, Yi; Duan, Rui
  作者单位：Columbia University; Columbia University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Transfer learning improves target model performance by leveraging data from related source populations, especially when target data are scarce. This study addresses the challenge of training high-dimensional regression models with limited target data in the presence of heterogeneous source populations. We focus on a practical setting where only parameter estimates of pretrained source models are available, rather than individual-level source data. For a single source model, we propose a novel ...
• Detection and inference of changes in high-dimensional linear regression with nonsparse structures AI翻译
  作者：Cho, Haeran; Kley, Tobias; Li, Housen
  作者单位：University of Bristol; University of Gottingen
  摘要：For data segmentation in high-dimensional linear regression settings, the regression parameters are often assumed to be exactly sparse segment-wise, which enables many existing methods to estimate the parameters locally via & ell;1-regularized maximum-likelihood-type estimation and then contrast them for change point detection. Contrary to this common practice, we show that the exact sparsity of neither regression parameters nor their differences, a.k.a. differential parameters, is necessary f...
• Bayesian analysis of product feature allocation models AI翻译
  作者：Ghilotti, Lorenzo; Camerlenghi, Federico; Rigon, Tommaso
  作者单位：University of Milano-Bicocca
  摘要：Feature allocation models are an extension of Bayesian nonparametric clustering models, where individuals can share multiple features. We study a broad class of models whose probability distribution has a product form, which includes the popular Indian buffet process. This class plays a prominent role among existing priors, and it shares structural characteristics with Gibbs-type priors in the species sampling framework. We develop a general theory for the entire class, obtaining closed form e...
• Doubly robust conditional independence testing with generative neural networks AI翻译
  作者：Zhang, Yi; Huang, Linjun; Yang, Yun; Shao, Xiaofeng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University System of Maryland; University of Maryland College Park; Washington University (WUSTL); Washington University (WUSTL)
  摘要：This article addresses the problem of testing the conditional independence of two generic random vectors X and Y given a third random vector Z, which plays an important role in statistical and machine learning applications. We propose a new non-parametric testing procedure that avoids explicitly estimating any conditional distributions but instead requires sampling from the two marginal conditional distributions of X given Z and Y given Z. We further propose using a generative neural network (...
• Parameterizing and simulating from causal models (vol. 86, pg. 535, 2024) AI翻译
  作者：Evans, Robin J.; Didelez, Vanessa
• Ordinary differential equation models for a collection of discretized functions AI翻译
  作者：Shao, Lingxuan; Yao, Fang
  作者单位：Fudan University; Peking University
  摘要：The exploration of dynamic systems governed by ordinary differential equations (ODEs) holds great interest in the field of statistics. Existing research mainly focuses on a single function. This study generalizes the scope to analyse a collection of functions observed at discretized times, with sampling frequencies varying from sparse to dense designs. The range of ODE models studied caters to diverse dynamic systems, and includes the complex nonlinear and non-Lipschitz scenarios. We introduce...