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作者：Gui, Lin; Jiang, Yuchao; Wang, Jingshu
作者单位：University of Chicago; Texas A&M University System; Texas A&M University College Station
摘要：Combining dependent $ p $-values poses a long-standing challenge in statistical inference, particularly when aggregating findings from multiple methods to enhance signal detection. Recently, $ p $-value combination tests based on regularly-varying-tailed distributions, such as the Cauchy combination test and harmonic mean $ p $-value, have attracted attention for their robustness to unknown dependence. This paper provides a theoretical and empirical evaluation of these methods under an asympto...
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作者：Gao, Chenyin; Yang, Shu; Shan, Mingyang; Ye, Wenyu; Lipkovich, Ilya; Faries, Douglas
作者单位：North Carolina State University; Eli Lilly; Lilly Research Laboratories
摘要：In recent years, real-world external controls have grown in popularity as a tool to empower randomized placebo-controlled trials, particularly in rare diseases or cases where balanced randomization is unethical or impractical. However, as external controls are not always comparable to the trials, direct borrowing without scrutiny may heavily bias the treatment effect estimator. Our paper proposes a data-adaptive integrative framework capable of preventing unknown biases of the external control...
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作者：Qu, Tianyi; Du, Jiangchuan; Li, Xinran
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Chicago
摘要：Randomized experiments have been the gold standard for drawing causal inference. The conventional model-based approach has been one of the most popular methods of analysing treatment effects from randomized experiments, which is often carried out through inference for certain model parameters. In this paper, we provide a systematic investigation of model-based analyses for treatment effects under the randomization-based inference framework. This framework does not impose any distributional ass...
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作者：Chatterjee, A.; Bhattacharya, B. B.
作者单位：University of Pennsylvania
摘要：The kernel two-sample test based on the maximum mean discrepancy is one of the most popular methods for detecting differences between two distributions over general metric spaces. In this paper we propose a method to boost the power of the kernel test by combining maximum mean discrepancy estimates over multiple kernels using their Mahalanobis distance. We derive the asymptotic null distribution of the proposed test statistic and use a multiplier bootstrap approach to efficiently compute the r...
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作者：Zhang, Yaowu; Zhu, Liping
作者单位：Shanghai University of Finance & Economics; Renmin University of China
摘要：Testing independence between high-dimensional random vectors is fundamentally different from testing independence between univariate random variables. Taking the projection correlation as an example, it suffers from at least three problems. First, it has a high computational complexity of O{n3(p+q)}, where n, p and q are the sample size and dimensions of the random vectors; this limits its usefulness substantially when n is extremely large. Second, the asymptotic null distribution of the proje...
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作者：Battey, H. S.; Mccullagh, Peter
作者单位：Imperial College London; University of Chicago
摘要：It is frequently observed in practice that the Wald statistic gives a poor assessment of the statistical significance of a variance component. This paper provides detailed analytic insight into the phenomenon by way of two simple models, which point to an atypical geometry as the source of the aberration. The latter can in principle be checked numerically to cover situations of arbitrary complexity, such as those arising from elaborate forms of blocking in an experimental context, or models fo...
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作者：Wood, S. N.
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作者：Zhou, Ying; Tang, Dingke; Kong, Dehan; Wang, Linbo
作者单位：University of Connecticut; University of Toronto
摘要：A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we introduce a novel approach for causal inference that leverages information in multiple outcomes to deal with unmeasured confounding. An important assumption in our approach is conditional independence among multiple outcomes. In contrast to existing proposals in the literature, the roles of multiple outcomes in the co...
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作者：Guedon, T.; Baey, C.; Kuhn, E.
作者单位：Universite Paris Saclay; INRAE; Universite de Lille; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
摘要：We examine the problem of variance component testing in general mixed effects models using the likelihood ratio test. We account for the presence of nuisance parameters, ie, the fact that some untested variances might also be equal to zero. Two main issues arise in this context, leading to a nonregular setting. First, under the null hypothesis, the true parameter value lies on the boundary of the parameter space. Moreover, due to the presence of nuisance parameters, the exact locations of thes...
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作者：Li, Yicheng; Zhang, Haobo; Lin, Qian
作者单位：Tsinghua University
摘要：One of the most interesting problems in the recent renaissance of the studies in kernel regression might be whether kernel interpolation can generalize well, since it may help us understand the 'benign overfitting phenomenon' reported in the literature on deep networks. In this paper, under mild conditions, we show that, for any epsilon>0, the generalization error of kernel interpolation is lower bounded by Omega(n(-epsilon)). In other words, the kernel interpolation generalizes poorly for a l...
