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作者：Rytgaard, H. C. W.; van der Laan, M. J.
作者单位：University of Copenhagen; University of California System; University of California Berkeley
摘要：This paper considers the one-step targeted maximum likelihood estimation methodology for multi-dimensional causal parameters in general survival and competing risk settings where event times take place on the positive real line and are subject to right censoring. We focus on effects of baseline treatment decisions possibly confounded by pretreatment covariates, but remark that our work generalizes to settings with time-varying treatment regimes and time-dependent confounding. We point out two ...
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作者：Cui, Y.; Tchetgen, E. J. Tchetgen
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作者：Wiens, D. P.
作者单位：University of Alberta
摘要：We present a result according to which certain functions of covariance matrices are maximized at scalar multiples of the identity matrix. This is used to show that experimental designs that are optimal under an assumption of independent, homoscedastic responses can be minimax robust, in broad classes of alternate covariance structures. In particular, it can justify the common practice of disregarding possible dependence, or heteroscedasticity, at the design stage of an experiment.
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作者：Hanley, J. A.
作者单位：McGill University
摘要：Statisticians and epidemiologists generally cite the publications of and as the first description and use of conditional logistic regression, while economists cite the book chapter by Nobel laureate McFadden (). We describe the until-now-unrecognized use of, and way of fitting, this model in 1934 by Lionel Penrose and Ronald Fisher.
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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：Maestrini, Luca; Bhaskaran, Aishwarya; Wand, Matt P.
作者单位：Australian National University; University of Technology Sydney
摘要：A recent article by on generalized linear mixed model asymptotics derived the rates of convergence for the asymptotic variances of maximum likelihood estimators. If m denotes the number of groups and n is the average within-group sample size then the asymptotic variances have orders m-1 and (mn)-1, depending on the parameter. We extend this theory to provide explicit forms of the (mn)-1 second terms of the asymptotically harder-to-estimate parameters. Improved accuracy of statistical inference...
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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：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...
