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作者：Xie, Fangzheng; Wu, Dingbo
作者单位：Indiana University System; Indiana University Bloomington
摘要：In this paper, we develop an eigenvector-assisted estimation framework for a collection of signal-plus-noise matrix models arising in high-dimensional statistics and many applications. The framework is built upon a novel asymptotically unbiased estimating equation using the leading eigenvectors of the data matrix. However, the estimator obtained by directly solving the estimating equation could be numerically unstable in practice and lacks robustness against model misspecification. We propose ...
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作者：Panigrahi, Snigdha; Fry, Kevin; Taylor, Jonathan
作者单位：University of Michigan System; University of Michigan; Stanford University
摘要：We introduce a pivot for exact selective inference with randomization. Not only does our pivot lead to exact inference in Gaussian regression models, but it is also available in closed form. We reduce this problem to inference for a bivariate truncated Gaussian variable. By doing so, we give up some power that is achieved with approximate maximum likelihood estimation in . Yet our pivot always produces narrower confidence intervals than a closely related data-splitting procedure. We investigat...
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作者：Hu, Y.; Wang, W.
作者单位：Southern Methodist University; National University of Singapore
摘要：Community detection is a crucial task in network analysis that can be significantly improved by incorporating subject-level information, ie, covariates. Existing methods have shown the effectiveness of using covariates on the low-degree nodes, but rarely discuss the case where communities have significantly different density levels, ie, multiscale networks. In this paper, we introduce a novel method that addresses this challenge by constructing network-adjusted covariates, which leverage the n...
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作者：Leung, Michael P.
作者单位：University of California System; University of California Santa Cruz
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作者：Sadeghi, Kayvan; Soo, Terry
作者单位：University of London; University College London
摘要：Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of do-calculus in the case of structure causal models. We provide simple axiomatizations for families of probability distributions to be different types of interventional distributions. Our axiomatizations neatly lead to a simple and clear theory of causality that has several advantages: it does not need to make use of any modelling assumptions such as those imposed by structural causal models; it r...
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作者：Wen, Mengtao; Jia, Yinxu; Ren, Haojie; Wang, Zhaojun; Zou, Changliang
作者单位：Nankai University; Shanghai Jiao Tong University
摘要：This study addresses the challenge of distribution estimation and inference in a semi-supervised setting. In contrast to prior research focusing on parameter inference, this work explores the complexities of semi-supervised distribution estimation, particularly the uniformity problem inherent in functional processes. To tackle this issue, we introduce a versatile framework designed to extract valuable information from unlabelled data by approximating a conditional distribution on covariates. T...
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作者：Wei, Keyao; Wang, Lengyang; Xia, Yingcun
作者单位：National University of Singapore; National Centre for Infectious Diseases Singapore
摘要：In practice, it is common for collected data to be underreported, an issue that is particularly prevalent in fields such as the social sciences, ecology and epidemiology. Drawing inferences from such data using conventional statistical methods can lead to incorrect conclusions. In this paper, we study tests for serial or cross dependence in time series data that are subject to underreporting. We introduce new test statistics, develop corresponding group-of-blocks bootstrap techniques and estab...
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作者：Ye, Ting; Shao, Jun; Yi, Yanyao
作者单位：University of Washington; University of Washington Seattle; University of Wisconsin System; University of Wisconsin Madison; Eli Lilly
摘要：Nonparametric covariate adjustment is considered for log-rank-type tests of the treatment effect with right-censored time-to-event data from clinical trials applying covariate-adaptive randomization. Our proposed covariate-adjusted log-rank test has a simple explicit formula and a guaranteed efficiency gain over the unadjusted test. We also show that our proposed test achieves universal applicability in the sense that the same formula of test can be universally applied to simple randomization ...
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作者：McElroy, Tucker; Politis, Dimitris N.
作者单位：University of California System; University of California San Diego
摘要：Over the last 35 years, several bootstrap methods for time series have been proposed. Popular time domain methods include the block bootstrap, the stationary bootstrap, the linear process bootstrap, among others; subsampling for time series is also available, and is closely related to the block bootstrap. The frequency domain bootstrap has been performed either by resampling the periodogram ordinates or by resampling the ordinates of the discrete Fourier transform. The paper at hand proposes a...
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作者：Lin, Zhexiao; Han, Fang
作者单位：University of California System; University of California Berkeley; University of Washington; University of Washington Seattle
摘要：While researchers commonly use the bootstrap to quantify the uncertainty of an estimator, it has been noticed that the standard bootstrap, in general, does not work for Chatterjee's rank correlation. In this paper, we provide proof of this issue under an additional independence assumption, and complement our theory with simulation evidence for general settings. Chatterjee's rank correlation thus falls into a category of statistics that are asymptotically normal, but bootstrap inconsistent. Val...
