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作者：Schwartz, Daniel; Saha, Riddhiman; Ventz, Steffen; Trippa, Lorenzo
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; University of Minnesota System; University of Minnesota Twin Cities
摘要：Subgroup analyses of randomized controlled trials (RCTs) constitute an important component of the drug development process in precision medicine. In particular, subgroup analyses of early-stage trials often influence the design and eligibility criteria of subsequent confirmatory trials and ultimately influence which subpopulations will receive the treatment after regulatory approval. However, subgroup analyses are often complicated by small sample sizes, which leads to substantial uncertainty ...
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作者：Levis, Alexander W.; Bonvini, Matteo; Zeng, Zhenghao; Keele, Luke; Kennedy, Edward H.
作者单位：Carnegie Mellon University; University of Pennsylvania
摘要：When an exposure of interest is confounded by unmeasured factors, an instrumental variable (IV) can be used to identify and estimate certain causal contrasts. Identification of the marginal average treatment effect (ATE) from IVs relies on strong untestable structural assumptions. When one is unwilling to assert such structure, IVs can nonetheless be used to construct bounds on the ATE. Famously, Alexander Balke and Judea Pearl proved tight bounds on the ATE for a binary outcome, in a randomiz...
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作者：Wang, Sunny G. W.; Patilea, Valentin; Klutchnikoff, Nicolas
作者单位：Centre National de la Recherche Scientifique (CNRS); Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI); Universite de Rennes; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite de Rennes
摘要：Functional data analysis almost always involves smoothing discrete observations into curves, because they are never observed in continuous time and rarely without error. Although smoothing parameters affect the subsequent inference, data-driven methods for selecting these parameters are not well-developed, frustrated by the difficulty of using all the information shared by curves while being computationally efficient. On the one hand, smoothing individual curves in an isolated, albeit sophisti...
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作者：Zhou, Yidong; Mueller, Hans-Georg
作者单位：University of California System; University of California Davis
摘要：Sparse functional/longitudinal data have attracted widespread interest due to the prevalence of such data in social and life sciences. A prominent scenario where such data are routinely encountered are accelerated longitudinal studies, where subjects are enrolled in the study at a random time and are only tracked for a short amount of time relative to the domain of interest. The statistical analysis of such functional snippets is challenging since information for far-off-diagonal regions of th...
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作者：Hore, Rohan; Barber, Rina Foygel
作者单位：University of Chicago
摘要：In this work, we consider the problem of building distribution-free prediction intervals with finite-sample conditional coverage guarantees. Conformal prediction (CP) is an increasingly popular framework for building such intervals with distribution-free guarantees, but these guarantees only ensure marginal coverage: the probability of coverage is averaged over both the training and test data, meaning that there might be substantial undercoverage within certain subpopulations. Instead, ideally...
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作者：Salomone, Robert; South, Leah F.; Drovandi, Christopher; Kroese, Dirk P.; Johansen, Adam M.
作者单位：Queensland University of Technology (QUT); Queensland University of Technology (QUT); University of Queensland; University of Warwick
摘要：We introduce a new class of sequential Monte Carlo methods which reformulates the essence of the nested sampling (NS) method of Skilling in terms of sequential Monte Carlo techniques. Two new algorithms are proposed: nested sampling via sequential Monte Carlo (NS-SMC) and adaptive nested sampling via sequential Monte Carlo (ANS-SMC). The new framework allows convergence results to be obtained in the setting when Markov chain Monte Carlo (MCMC) is used to produce new samples. An additional bene...
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作者：Dai, Xiaowu
作者单位：University of California System; University of California Los Angeles
摘要：Traditional nonparametric estimation methods often lead to a slow convergence rate in large dimensions and require unrealistically large dataset sizes for reliable conclusions. We develop an approach based on partial derivatives, either observed or estimated, to effectively estimate the function at near-parametric convergence rates. This novel approach and computational algorithm could lead to methods useful to practitioners in many areas of science and engineering. Our theoretical results rev...
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作者：Yan, Yuling; Su, Weijie J.; Fan, Jianqing
作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Pennsylvania; Princeton University
摘要：In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their papers by perceived quality. In this paper, we leverage these author-specified rankings to enhance peer review in machine learning and artificial intelligence conferences by extending the isotonic mechanism to exponential family distributions. This mechanism produces adjusted scores closely aligned with the original scores while strictly adhering to the author-specified ran...
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作者：Zhang, Yichi; Yang, Shu
作者单位：Indiana University System; Indiana University Bloomington; North Carolina State University
摘要：Principal stratification is essential for revealing causal mechanisms involving post-treatment intermediate variables, in real-world applications like surrogate marker evaluation. Principal stratification analysis with continuous intermediate variables is increasingly common but challenging due to the infinite principal strata and the nonidentifiability and nonregularity of principal causal effects (PCEs). Inspired by recent research, we resolve these challenges by first using a flexible copul...
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作者：Lin, Xiaotong; Li, Weihao; Tian, Fangqiao; Huang, Dongming
作者单位：National University of Singapore; National University of Singapore
摘要：We introduce a general framework for testing goodness-of-fit for Gaussian graphical models in both the low- and high-dimensional settings. This framework is based on a novel algorithm for generating exchangeable copies by conditioning on sufficient statistics. This framework provides exact finite-sample error control regardless of the dimension and allows flexible choices of test statistics to improve power. We explore several candidate test statistics and conduct extensive simulation studies ...
