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作者：Hong, Shaoxin; Jiang, Jiancheng; Jiang, Xuejun; Wang, Haofeng
作者单位：Shandong University; University of North Carolina; University of North Carolina Charlotte; Southern University of Science & Technology
摘要：It is routine practice in statistical modelling to first select variables and then make inference for the selected model as in stepwise regression. Such inference is made upon the assumption that the selected model is true. However, without this assumption, one would not know the validity of the inference. Similar problems also exist in high-dimensional regression with regularization. To address these problems, we propose a dimension-reduced generalized likelihood ratio test for generalized li...
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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：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：Gabriel, Erin E.; Sachs, Michael C.; Jensen, Andreas Kryger
作者单位：University of Copenhagen
摘要：The probability of benefit can be a valuable and meaningful measure of treatment effect. Particularly for an ordinal outcome, it can have an intuitive interpretation. Unfortunately, this measure, and variations of it, are not identifiable even in randomized trials with perfect compliance. There is, for this reason, a long literature on nonparametric bounds for unidentifiable measures of benefit. These have primarily focused on perfect randomized trial settings and one or two specific estimands...
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作者：Dilernia, A. S.; Fiecas, M.; Zhang, L.
作者单位：Grand Valley State University; University of Minnesota System; University of Minnesota Twin Cities
摘要：We derive an asymptotic joint distribution and novel covariance estimator for the partial correlations of a multivariate Gaussian time series given mild regularity conditions. Using our derived asymptotic distribution, we develop a Wald confidence interval and testing procedure for inference of individual partial correlations for time series data. Through simulation we demonstrate that our proposed confidence interval attains higher coverage rates, and our testing procedure attains false posit...
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作者：Koning, Nick W.
作者单位：Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC
摘要：It is conventionally believed that permutation-based testing methods should ideally use all permutations. We challenge this by showing that we can sometimes obtain dramatically more power by using a tiny subgroup. As the subgroup is tiny, this also comes at a much lower computational cost. Moreover, the method remains valid for the same hypotheses. We exploit this to improve the popular permutation-based Westfall and Young MaxT multiple testing method. We analyse the relative efficiency in a G...
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作者：Henzi, Alexander; Law, Michael
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：We consider the problem of independence testing for two univariate random variables in a sequential setting. By leveraging recent developments on safe, anytime-valid inference, we propose a test with time-uniform Type-I error control and derive explicit bounds on the finite-sample performance of the test. We demonstrate the empirical performance of the procedure in comparison to existing sequential and nonsequential independence tests. Furthermore, since the proposed test is distribution-free ...
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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...
