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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：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：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：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：Singh, R.; Xu, L.; Gretton, A.
作者单位：Massachusetts Institute of Technology (MIT); University of London; University College London
摘要：We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous and incremental response curves. The treatment and covariates may be discrete or continuous in general spaces. Because of a decomposition property specific to the reproducing kernel Hilbert space, our estimators have simple closed-form solutions. We prove uniform consistency with finite sample rates via an original analysis of generalized kernel ridge regression. We extend our ...
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作者：Aue, Alexander; Kirch, Claudia
作者单位：University of California System; University of California Davis; Otto von Guericke University
摘要：Quality control charts aim at raising an alarm as soon as sequentially obtained observations of an underlying random process no longer seem to be within stochastic fluctuations prescribed by an in-control scenario. Such random processes can often be modelled using the concept of stationarity, or even independence as in most classical works. An important out-of-control scenario is the changepoint alternative, for which the distribution of the process changes at an unknown point in time. In his ...
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作者：Cui, Y.; Tchetgen, E. J. Tchetgen
作者单位：Zhejiang University; Zhejiang University; University of Pennsylvania
摘要：While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we propose a selective machine learning framework for making inferences about a finite-dimensional functional defined on a semiparametric model, when the latter admits a doubly robust estimating function and several candidate machine learning algorithms are avai...
