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作者：Wang, Shulei; Yuan, Bo; Cai, T. Tony; Li, Hongzhe
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Pennsylvania; University of Pennsylvania
摘要：Phylogenetic association analysis plays a crucial role in investigating the correlation between microbial compositions and specific outcomes of interest in microbiome studies. However, existing methods for testing such associations have limitations related to the assumption of a linear association in high-dimensional settings and the handling of confounding effects. Hence, there is a need for methods capable of characterizing complex associations, including nonmonotonic relationships. This art...
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作者：Gu, Yu; Zeng, Donglin; Heiss, Gerardo; Lin, D. Y.
作者单位：University of Hong Kong; University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：Interval-censored multistate data arise in many studies of chronic diseases, where the health status of a subject can be characterized by a finite number of disease states and the transition between any two states is only known to occur over a broad time interval. We relate potentially time-dependent covariates to multistate processes through semiparametric proportional intensity models with random effects. We study nonparametric maximum likelihood estimation under general interval censoring a...
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作者：Feng, Long; Yang, Guang
作者单位：University of Hong Kong; City University of Hong Kong
摘要：We develop a novel framework for the analysis of medical imaging data, including magnetic resonance imaging, functional magnetic resonance imaging, computed tomography and more. Medical imaging data differ from general images in two main aspects: (i) the sample size is often considerably smaller and (ii) the interpretation of the model is usually more crucial than predicting the outcome. As a result, standard methods such as convolutional neural networks cannot be directly applied to medical i...
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作者：Yang, Cheng-Han; Cheng, Yu-Jen
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作者：Astfalck, Lachlan C.; Sykulski, Adam M.; Cripps, Edward J.
作者单位：University of Western Australia; Imperial College London
摘要：Welch's method provides an estimator of the power spectral density that is statistically consistent. This is achieved by averaging over periodograms calculated from overlapping segments of a time series. For a finite-length time series, while the variance of the estimator decreases as the number of segments increases, the magnitude of the estimator's bias increases: a bias-variance trade-off ensues when setting the segment number. We address this issue by providing a novel method for debiasing...
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作者：Zhao, Haibing; Zhou, Huijuan
作者单位：Shanghai University of Finance & Economics
摘要：In the field of multiple hypothesis testing, auxiliary information can be leveraged to enhance the efficiency of test procedures. A common way to make use of auxiliary information is by weighting p-values. However, when the weights are learned from data, controlling the finite-sample false discovery rate becomes challenging, and most existing weighted procedures only guarantee false discovery rate control in an asymptotic limit. In a recent study conducted by , a novel tau-censored weighted Be...
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作者：Diaz, I; Hejazi, N. S.; Rudolph, K. E.; van Der Laan, M. J.
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作者：Ignatiadis, Nikolaos; Wang, Ruodu; Ramdas, Aaditya
作者单位：University of Chicago; University of Chicago; University of Waterloo; Carnegie Mellon University
摘要：We study how to combine p-values and e-values, and design multiple testing procedures where both p-values and e-values are available for every hypothesis. Our results provide a new perspective on multiple testing with data-driven weights: while standard weighted multiple testing methods require the weights to deterministically add up to the number of hypotheses being tested, we show that this normalization is not required when the weights are e-values that are independent of the p-values. Such...
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作者：Jiang, Feiyu; Gao, Hanjia; Shao, Xiaofeng
作者单位：Fudan University; University of Illinois System; University of Illinois Urbana-Champaign
摘要：We propose a novel method for testing serial independence of object-valued time series in metric spaces, which are more general than Euclidean or Hilbert spaces. The proposed method is fully nonparametric, free of tuning parameters and can capture all nonlinear pairwise dependence. The key concept used in this paper is the distance covariance in metric spaces, which is extended to the autodistance covariance for object-valued time series. Furthermore, we propose a generalized spectral density ...
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作者：Li, Guanxun; Zhang, Xianyang
作者单位：Beijing Normal University; Beijing Normal University Zhuhai; Texas A&M University System; Texas A&M University College Station
摘要：We discover a connection between the Benjamini-Hochberg procedure and the e-Benjamini-Hochberg procedure (Wang & Ramdas, 2022) with a suitably defined set of e-values. This insight extends to Storey's procedure and generalized versions of the Benjamini-Hochberg procedure and the model-free multiple testing procedure of Barber & Cand & eacute;s (2015) with a general form of rejection rules. We further summarize these findings in a unified form. These connections open up new possibilities for de...
