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作者：Karwa, Vishesh; Pati, Debdeep; Petrovic, Sonja; Solus, Liam; Alexeev, Nikita; Raic, Mateja; Wilburne, Dane; Williams, Robert; Yan, Bowei
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Texas A&M University System; Texas A&M University College Station; Illinois Institute of Technology; Royal Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; MITRE Corporation; Rose Hulman Institute Technology; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：We construct Bayesian and frequentist finite-sample goodness-of-fit tests for three different variants of the stochastic blockmodel for network data. Since all of the stochastic blockmodel variants are log-linear in form when block assignments are known, the tests for the latent block model versions combine a block membership estimator with the algebraic statistics machinery for testing goodness-of-fit in log-linear models. We describe Markov bases and marginal polytopes of the variants of the...
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作者：Pace, Luigi; Salvan, Alessandra
作者单位：University of Udine; University of Padua
摘要：We develop the theory of hypothesis testing based on the e-value, a notion of evidence that, unlike the p-value, allows for effortlessly combining results from several studies in the common scenario where the decision to perform a new study may depend on previous outcomes. Tests based on e-values are safe, i.e. they preserve type-I error guarantees, under such optional continuation. We define growth rate optimality (GAO) as an analogue of power in an optional continuation context, and we show ...
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作者：Young, Elliot H.; Shah, Rajen D.
作者单位：University of Cambridge
摘要：We study partially linear models in settings where observations are arranged in independent groups but may exhibit within-group dependence. Existing approaches estimate linear model parameters through weighted least squares, with optimal weights (given by the inverse covariance of the response, conditional on the covariates) typically estimated by maximizing a (restricted) likelihood from random effects modelling or by using generalized estimating equations. We introduce a new 'sandwich loss' ...
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作者：Ye, Ting; Liu, Zhonghua; Sun, Baoluo; Tchetgen, Eric Tchetgen
作者单位：University of Washington; University of Washington Seattle; Columbia University; National University of Singapore; University of Pennsylvania; University of Washington; University of Washington Seattle
摘要：Mendelian randomization (MR) addresses causal questions using genetic variants as instrumental variables. We propose a new MR method, G-Estimation under No Interaction with Unmeasured Selection (GENIUS)-MAny Weak Invalid IV, which simultaneously addresses the 2 salient challenges in MR: many weak instruments and widespread horizontal pleiotropy. Similar to MR-GENIUS, we use heteroscedasticity of the exposure to identify the treatment effect. We derive influence functions of the treatment effec...
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作者：Tran, Ngoc Mai; Buck, Johannes; Klueppelberg, Claudia
作者单位：University of Texas System; University of Texas Austin; Technical University of Munich
摘要：We propose a new method to estimate a root-directed spanning tree from extreme data. Prominent example is a river network, to be discovered from extreme flow measured at a set of stations. Our new algorithm utilizes qualitative aspects of a max-linear Bayesian network, which has been designed for modelling causality in extremes. The algorithm estimates bivariate scores and returns a root-directed spanning tree. It performs extremely well on benchmark data and on new data. We prove that the new...
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作者：Kosmidis, Ioannis; Lunardon, Nicola
作者单位：University of Warwick; Universita Ca Foscari Venezia
摘要：We develop a novel, general framework for reduced-bias M-estimation from asymptotically unbiased estimating functions. The framework relies on an empirical approximation of the bias by a function of derivatives of estimating function contributions. Reduced-bias M-estimation operates either implicitly, solving empirically adjusted estimating equations, or explicitly, subtracting the estimated bias from the original M-estimates, and applies to partially or fully specified models with likelihoods...
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作者：Dickhaus, Thorsten
作者单位：Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS); University of Bremen
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作者：Celentano, Michael; Montanari, Andrea
作者单位：University of California System; University of California Berkeley; Stanford University; Stanford University
摘要：We consider the problem of estimating a low-dimensional parameter in high-dimensional linear regression. Constructing an approximately unbiased estimate of the parameter of interest is a crucial step towards performing statistical inference. Several authors suggest to orthogonalize both the variable of interest and the outcome with respect to the nuisance variables, and then regress the residual outcome with respect to the residual variable. This is possible if the covariance structure of the ...
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作者：Qiu, Yumou; Guo, Bin
作者单位：Peking University; Peking University; Southwestern University of Finance & Economics - China; Peking University; Peking University
摘要：In this article, we derive the minimax detection boundary for testing a sub-block of variables in a precision matrix under the Gaussian distribution. Compared to the results on the minimum rate of signals for testing precision matrices in literature, our result gives the exact minimum signal strength in a precision matrix that can be detected. We propose a thresholding test that is able to achieve the minimax detection boundary under certain cases by adaptively choosing the threshold level. Th...
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作者：Martin, Ryan
作者单位：North Carolina State University
摘要：We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical problem of estimating a bounded mean. Our approach generalizes and improves on the celebrated Chernoff method, yielding the best closed-form empirical-Bernstein CSs and CIs (converging exactly to the oracle Bernstein width) as well as non-closed-form betting CSs and CIs. Our method combines new composite nonnegative (super) martingales with Ville's maximal inequality, with strong connections to testing by bet...
