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作者：Song, Hoseung; Chen, Hao
作者单位：University of California System; University of California Davis
摘要：Kernel two-sample tests have been widely used for multivariate data to test equality of distributions. However, existing tests based on mapping distributions into a reproducing kernel Hilbert space mainly target specific alternatives and do not work well for some scenarios when the dimension of the data is moderate to high due to the curse of dimensionality. We propose a new test statistic that makes use of a common pattern under moderate and high dimensions and achieves substantial power impr...
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作者：Li, Wei; Lu, Zitong; Jia, Jinzhu; Xie, Min; Geng, Zhi
作者单位：Renmin University of China; Renmin University of China; City University of Hong Kong; Peking University; Peking University; Beijing Technology & Business University
摘要：As highlighted in and , deducing the causes of given effects is a more challenging problem than evaluating the effects of causes in causal inference. proposed an approach for deducing causes of a single effect variable based on posterior causal effects. In many applications, there are multiple effect variables, and they can be used simultaneously to more accurately deduce the causes. To retrospectively deduce causes from multiple effects, we propose multivariate posterior total, intervention a...
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作者：Branson, Zach; Li, Xinran; Ding, Peng
作者单位：Carnegie Mellon University; University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Berkeley
摘要：Power analyses are an important aspect of experimental design, because they help determine how experiments are implemented in practice. It is common to specify a desired level of power and compute the sample size necessary to obtain that power. Such calculations are well known for completely randomized experiments, but there can be many benefits to using other experimental designs. For example, it has recently been established that rerandomization, where subjects are randomized until covariate...
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作者：Savje, F.
作者单位：Yale University
摘要：Exposure mappings facilitate investigations of complex causal effects when units interact in experiments. Current methods require experimenters to use the same exposure mappings to define the effect of interest and to impose assumptions on the interference structure. However, the two roles rarely coincide in practice, and experimenters are forced to make the often questionable assumption that their exposures are correctly specified. This paper argues that the two roles exposure mappings curren...
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作者：Zhu, Yichen; Peruzzi, Michele; Li, Cheng; Dunson, David B.
作者单位：Bocconi University; University of Michigan System; University of Michigan; National University of Singapore; Duke University
摘要：In geostatistical problems with massive sample size, Gaussian processes can be approximated using sparse directed acyclic graphs to achieve scalable O(n) computational complexity. In these models, data at each location are typically assumed conditionally dependent on a small set of parents that usually include a subset of the nearest neighbours. These methodologies often exhibit excellent empirical performance, but the lack of theoretical validation leads to unclear guidance in specifying the ...
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作者：Hemerik, Jesse; Solari, Aldo; Goeman, Jelle J.
作者单位：Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; Universita Ca Foscari Venezia; Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC)
摘要：We introduce a multiple testing procedure that controls the median of the proportion of false discoveries in a flexible way. The procedure requires only a vector of p-values as input and is comparable to the Benjamini-Hochberg method, which controls the mean of the proportion of false discoveries. Our method allows free choice of one or several values of $ \alpha $ after seeing the data, unlike the Benjamini-Hochberg procedure, which can be very anti-conservative when $ \alpha $ is chosen post...
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作者：Klockmann, K.; Krivobokova, T.
作者单位：University of Vienna
摘要：A new efficient nonparametric estimator for Toeplitz covariance matrices is proposed. This estimator is based on a data transformation that translates the problem of Toeplitz covariance matrix estimation to the problem of mean estimation in an approximate Gaussian regression. The resulting Toeplitz covariance matrix estimator is positive definite by construction, fully data driven and computationally very fast. Moreover, this estimator is shown to be minimax optimal under the spectral norm for...
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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...
