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作者：Taylor, Jeremy M. G.; Choi, Kyuseong; Han, Peisong
作者单位：University of Michigan System; University of Michigan; Cornell University
摘要：We consider the situation of estimating the parameters in a generalized linear prediction model, from an internal dataset, where the outcome variable Y is binary and there are two sets of covariates, X and Z. We have information from an external study that provides parameter estimates for a generalized linear model of Y on X. We propose a method that makes limited assumptions about the similarity of the distributions in the two study populations. The method involves orthogonalizing the Z varia...
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作者：Masak, T.; Sarkar, S.; Panaretos, V. M.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：The nonparametric estimation of covariance lies at the heart of functional data analysis, whether for curve or surface-valued data. The case of a two-dimensional domain poses both statistical and computational challenges, which are typically alleviated by assuming separability. However, separability is often questionable, sometimes even demonstrably inadequate. We propose a framework for the analysis of covariance operators of random surfaces that generalizes separability while retaining its m...
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作者：Zhou, Zheng; Zhou, Yongdao
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作者：Guan, Leying
作者单位：Yale University
摘要：We propose a new inference framework called localized conformal prediction. It generalizes the framework of conformal prediction by offering a single-test-sample adaptive construction that emphasizes a local region around this test sample, and can be combined with different conformal scores. The proposed framework enjoys an assumption-free finite sample marginal coverage guarantee, and it also offers additional local coverage guarantees under suitable assumptions. We demonstrate how to change ...
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作者：Basse, Guillaume W.; Ding, Yi; Toulis, Panos
作者单位：Stanford University; Massachusetts Institute of Technology (MIT); University of Chicago
摘要：In many modern settings, such as an online marketplace, randomized experiments need to be executed over multiple time periods. In such temporal experiments, it has been observed that the effects of an intervention on an experimental unit may be large when the unit is first exposed to it, but then it attenuates after repeated exposures. This is typically due to units' habituation to the intervention, or some other form of learning, such as when users gradually start to ignore repeated mails sen...
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作者：Azriel, D.
作者单位：Technion Israel Institute of Technology
摘要：This work studies an experimental design problem where the values of a predictor variable, denoted by x, are to be determined with the goal of estimating a function m(x), which is observed with noise. A linear model is fitted to m(x), but it is not assumed that the model is correctly specified. It follows that the quantity of interest is the best linear approximation of m(x), which is denoted by l(x). It is shown that in this framework the ordinary least squares estimator typically leads to an...
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作者：Fomichov, V; Ivanovs, J.
作者单位：Aarhus University
摘要：There is growing empirical evidence that spherical k-means clustering performs well at identifying groups of concomitant extremes in high dimensions, thereby leading to sparse models. We provide one of the first theoretical results supporting this approach, but also demonstrate some pitfalls. Furthermore, we show that an alternative cost function may be more appropriate for identifying concomitant extremes, and it results in a novel spherical k-principal-components clustering algorithm. Our ma...
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作者：Lunde, Robert; Sarkar, Purnamrita
作者单位：University of Michigan System; University of Michigan; University of Texas System; University of Texas Austin
摘要：We study the properties of two subsampling procedures for networks, vertex subsampling and p-subsampling, under the sparse graphon model. The consistency of network subsampling is demonstrated under the minimal assumptions of weak convergence of the corresponding network statistics and an expected subsample size growing to infinity more slowly than the number of vertices in the network. Furthermore, under appropriate sparsity conditions, we derive limiting distributions for the nonzero eigenva...
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作者：Kwon, Yeil; Zhao, Zhigen
作者单位：University of Central Arkansas; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：We consider the problem of empirical Bayes estimation of multiple variances when provided with sample variances. Assuming an arbitrary prior on the variances, we derive different versions of the Bayes estimators using different loss functions. For one particular loss function, the resulting Bayes estimator relies on the marginal cumulative distribution function of the sample variances only. When replacing it with the empirical distribution function, we obtain an empirical Bayes version called ...
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作者：Marrs, F. W.; Fosdick, B. K.; Mccormick, T. H.
作者单位：United States Department of Energy (DOE); Los Alamos National Laboratory; Colorado State University System; Colorado State University Fort Collins; University of Washington; University of Washington Seattle
摘要：Relational arrays represent measures of association between pairs of actors, often in varied contexts or over time. Trade flows between countries, financial transactions between individuals, contact frequencies between school children in classrooms and dynamic protein-protein interactions are all examples of relational arrays. Elements of a relational array are often modelled as a linear function of observable covariates. Uncertainty estimates for regression coefficient estimators, and ideally...
