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作者：Chu, J.; Lu, W.; Yang, S.
作者单位：North Carolina State University
摘要：Personalized decision-making, aiming to derive optimal treatment regimes based on individual characteristics, has recently attracted increasing attention in many fields, such as medicine, social services and economics. Current literature mainly focuses on estimating treatment regimes from a single source population. In real-world applications, the distribution of a target population can be different from that of the source population. Therefore, treatment regimes learned by existing methods ma...
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作者：Dunn, Robin; Ramdas, Aaditya; Balakrishnan, Sivaraman; Wasserman, Larry
作者单位：Novartis; Novartis USA; Carnegie Mellon University
摘要：The classical likelihood ratio test based on the asymptotic chi-squared distribution of the log-likelihood is one of the fundamental tools of statistical inference. A recent universal likelihood ratio test approach based on sample splitting provides valid hypothesis tests and confidence sets in any setting for which we can compute the split likelihood ratio statistic, or, more generally, an upper bound on the null maximum likelihood. The universal likelihood ratio test is valid in finite sampl...
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作者：Duanmu, Haosui; Roy, Daniel M.; Smith, Aaron
作者单位：Harbin Institute of Technology; University of California System; University of California Berkeley; University of Toronto; University of Ottawa
摘要：A matching prior at level 1 - a is a prior such that an associated 1 - a credible region is also a 1- a confidence set. We study the existence of matching priors for general families of credible regions. Our main result gives topological conditions under which matching priors for specific families of credible regions exist. Informally, we prove that, on compact parameter spaces, a matching prior exists if the so-called rejection-probability function is jointly continuous when we adopt the Wass...
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作者：Guan, Yawen; Page, Garritt L.; Reich, Brian J.; Ventrucci, Massimo; Yang, Shu
作者单位：University of Nebraska System; University of Nebraska Lincoln; Brigham Young University; North Carolina State University; University of Bologna
摘要：Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence between the exposure and the unmeasured confounder that ensure the effect of exposure is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. One assumption that ensures identifiability is that confoundin...
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作者：Zhou, Zheng; Zhou, Yongdao
作者单位：Nankai University
摘要：Row-column designs have been widely used in experiments involving double confounding. Among them, one that provides unconfounded estimation of all main effects and as many two-factor interactions as possible is preferred, and is called optimal. Most current work focuses on the construction of two-level row-column designs, while the corresponding optimality theory has been largely ignored. Moreover, most constructed designs contain at least one replicate of a full factorial design, which is not...
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作者：Shen, Guohao; Chen, Kani; Huang, Jian; Lin, Yuanyuan
作者单位：Hong Kong Polytechnic University; Hong Kong University of Science & Technology; University of Iowa; Chinese University of Hong Kong
摘要：We propose a linearized maximum rank correlation estimator for the single-index model. Unlike the existing maximum rank correlation and other rank-based methods, the proposed estimator has a closed-form expression, making it appealing in theory and computation. The proposed estimator is robust to outliers in the response and its construction does not need knowledge of the unknown link function or the error distribution. Under mild conditions, it is shown to be consistent and asymptotically nor...
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作者：Vogrinc, Jure; Livingstone, Samuel; Zanella, Giacomo
作者单位：University of Warwick; University of London; University College London; Bocconi University
摘要：We study the class of first-order locally balanced Metropolis-Hastings algorithms introduced in Livingstone & Zanella (2022). To choose a specific algorithm within the class, the user must select a balancing function g : R+ -> R+ satisfying g(t) = tg(1/t) and a noise distribution for the proposal increment. Popular choices within the class are the Metropolis-adjusted Langevin algorithm and the recently introduced Barker proposal. We first establish a general limiting optimal acceptance rate of...
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作者：Gutknecht, A. J.; Barnett, L.
作者单位：University of Gottingen; University of Sussex
摘要：The single-regression Granger-Geweke causality estimator has previously been shown to solve known problems associated with the more conventional likelihood ratio estimator; however, its sampling distribution has remained unknown. We show that, under the null hypothesis of vanishing Granger causality, the single-regression estimator converges to a generalized ?(2) distribution, which is well approximated by a & UGamma; distribution. We show that this holds too for Geweke's spectral causality av...
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作者：Lin, Z.; Han, F.
作者单位：University of Washington; University of Washington Seattle
摘要：The ingenious approach of Chatterjee (2021) to estimate a measure of dependence first proposed by Dette et al. (2013) based on simple rank statistics has quickly caught attention. This measure of dependence has the appealing property of being between 0 and 1, and being 0 or 1 if and only if the corresponding pair of random variables is independent or one is a measurable function of the other almost surely. However, more recent studies (Cao & Bickel 2020; Shi et al. 2022b) showed that independe...
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作者：Qiu, Yixuan; Lei, Jing; Roeder, Kathryn
作者单位：Shanghai University of Finance & Economics; Carnegie Mellon University
摘要：Sparse principal component analysis is an important technique for simultaneous dimensionality reduction and variable selection with high-dimensional data. In this work we combine the unique geometric structure of the sparse principal component analysis problem with recent advances in convex optimization to develop novel gradient-based sparse principal component analysis algorithms. These algorithms enjoy the same global convergence guarantee as the original alternating direction method of mult...
