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作者：Pesta, Michal
作者单位：Charles University Prague
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作者：Fu, Chenqi; Zhou, Shouhao; Lee, J. Jack
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Penn State Health; University of Texas System; UTMD Anderson Cancer Center
摘要：Interval-based designs represent cutting-edge adaptive methodologies for phase I clinical trials to identify the maximum tolerated dose (MTD). These designs exhibit robust performance comparable to more intricate, model-based designs, and their pretabulated decision rule enables them to be implemented as simply as the conventional algorithm-based designs. In this paper, we introduce the posterior predictive (PoP) design, a novel interval-based design that leverages advanced Bayesian predictive...
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作者：Liu, Siyan; Yeh, Chi-Kuang; Zhang, Xin; Tian, Qinglong; Li, Pengfei
作者单位：East China Normal University; University of Waterloo
摘要：This study introduces a new approach to addressing the positive and unlabeled (PU) data through the double exponential tilting model (DETM) under a transfer learning framework. Traditional methods often fall short because they only apply to the common distributions (CD) PU data (also known as the selected completely at random PU data), where the labeled positive and unlabeled positive data are assumed to be from the same distribution. In contrast, our DETM's dual structure effectively accommod...
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作者：Chen, Yang
作者单位：University of Michigan System; University of Michigan
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作者：Leiner, James; Duan, Boyan; Wasserman, Larry; Ramdas, Aaditya
作者单位：Carnegie Mellon University; Carnegie Mellon University; Alphabet Inc.; Google Incorporated
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作者：Furfaro, Emanuela
作者单位：University of Washington; University of Washington Seattle
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作者：Elmasri, Mohamad
作者单位：University of Toronto; Alan Turing Institute
摘要：Bayesian inference for undirected graphical models is mostly restricted to the class of decomposable graphs, as they enjoy a rich set of properties making them amenable to high-dimensional problems. While parameter inference is straightforward in this setup, inferring the underlying graph is a challenge driven by the computational difficulty in exploring the space of decomposable graphs. This work makes two contributions to address this problem. First, we provide sufficient and necessary condi...
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作者：Zhao, Zinan; Sun, Wenguang
作者单位：Zhejiang University; Zhejiang University; Zhejiang University
摘要：The effective utilization of structural information in data while ensuring statistical validity poses a significant challenge in false discovery rate (FDR) analyses. Conformal inference provides rigorous theory for grounding complex machine learning methods without relying on strong assumptions or highly idealized models. However, existing conformal methods have limitations in handling structured multiple testing, as their validity often requires the deployment of symmetric decision rules, whi...
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作者：Zhang, Shucong; Wang, Huiyuan; Lin, Wei
作者单位：University of International Business & Economics; University of Pennsylvania; Peking University; Peking University
摘要：High-dimensional compositional data are prevalent in many applications. The simplex constraint poses intrinsic challenges to inferring the conditional dependence relationships among the components forming a composition, as encoded by a large precision matrix. We introduce a precise specification of the compositional precision matrix and relate it to its basis counterpart, which is shown to be asymptotically identifiable under suitable sparsity assumptions. By exploiting this connection, we pro...
