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作者：Ding, Yi; Li, Yingying; Song, Rui
作者单位：University of Macau; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; North Carolina State University; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology
摘要：We establish a high-dimensional statistical learning framework for individualized asset allocation. Our proposed methodology addresses continuous-action decision-making with a large number of characteristics. We develop a discretization approach to model the effect of continuous actions and allow the discretization frequency to be large and diverge with the number of observations. The value function of continuous-action is estimated using penalized regression with our proposed generalized pena...
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作者：Lee, Chanwoo; Wang, Miaoyan
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：We consider the problem of structured tensor denoising in the presence of unknown permutations. Such data problems arise commonly in recommendation systems, neuroimaging, community detection, and multiway comparison applications. Here, we develop a general family of smooth tensor models up to arbitrary index permutations; the model incorporates the popular tensor block models and Lipschitz hypergraphon models as special cases. We show that a constrained least-squares estimator in the block-wis...
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作者：Song, Yan; Khalid, Zubair; Genton, Marc G.
作者单位：King Abdullah University of Science & Technology; Lahore University of Management Sciences
摘要：Earth system models (ESMs) are fundamental for understanding Earth's complex climate system. However, the computational demands and storage requirements of ESM simulations limit their utility. For the newly published CESM2-LENS2 data, which suffer from this issue, we propose a novel stochastic generator (SG) as a practical complement to the CESM2, capable of rapidly producing emulations closely mirroring training simulations. Our SG leverages the spherical harmonic transformation (SHT) to shif...
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作者：Chen, Sida; Finkenstaedt, Baerbel
作者单位：University of Warwick; MRC Biostatistics Unit; University of Cambridge
摘要：B-spline-based hidden Markov models employ B-splines to specify the emission distributions, offering a more flexible modeling approach to data than conventional parametric HMMs. We introduce a Bayesian framework for inference, enabling the simultaneous estimation of all unknown model parameters including the number of states. A parsimonious knot configuration of the B-splines is identified by the use of a trans-dimensional Markov chain sampling algorithm, while model selection regarding the nu...
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作者：Gao, Jiti; Peng, Bin; Yan, Yayi
作者单位：Monash University; Shanghai University of Finance & Economics
摘要：In this article, we propose a simple inferential method for a wide class of panel data models with a focus on such cases that have both serial correlation and cross-sectional dependence. In order to establish an asymptotic theory to support the inferential method, we develop some new and useful higher-order expansions, such as Berry-Esseen bound and Edgeworth Expansion, under a set of simple and general conditions. We further demonstrate the usefulness of these theoretical results by explicitl...
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作者：Ye, Zi; Harrar, Solomon W.
作者单位：Lehigh University; University of Kentucky; University of Kentucky
摘要：Investigating the differential effect of treatments in groups defined by patient characteristics is of paramount importance in personalized medicine research. In some studies, participants are first classified as having or not of the characteristic of interest by diagnostic tools, but such classifiers may not be perfectly accurate. The impact of diagnostic misclassification in statistical inference has been recently investigated in parametric model contexts and shown to introduce severe bias i...
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作者：Wu, Ben; Guo, Ying; Kang, Jian
作者单位：Renmin University of China; Emory University; University of Michigan System; University of Michigan
摘要：Blind source separation (BSS) aims to separate latent source signals from their mixtures. For spatially dependent signals in high-dimensional and large-scale data, such as neuroimaging, most existing BSS methods do not take into account the spatial dependence and the sparsity of the latent source signals. To address these major limitations, we propose a Bayesian spatial blind source separation (BSP-BSS) approach for neuroimaging data analysis. We assume the expectation of the observed images a...
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作者：Quan, Mingxue; Lin, Zhenhua
作者单位：Renmin University of China; National University of Singapore
摘要：For nonparametric regression in the streaming setting, where data constantly flow in and require real-time analysis, a main challenge is that data are cleared from the computer system once processed due to limited computer memory and storage. We tackle the challenge by proposing a novel one-pass estimator based on penalized orthogonal basis expansions and developing a general framework to study the interplay between statistical efficiency and memory consumption of estimators. We show that, the...
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作者：Guerrier, Stephane; Kuzmics, Christoph; Victoria-Feser, Maria-Pia
作者单位：University of Geneva; University of Geneva; University of Graz; University of Bologna
摘要：Countries officially record the number of COVID-19 cases based on medical tests of a subset of the population. These case count data obviously suffer from participation bias, and for prevalence estimation, these data are typically discarded in favor of infection surveys, or possibly also completed with auxiliary information. One exception is the series of infection surveys recorded by the Statistics Austria Federal Institute to study the prevalence of COVID-19 in Austria in April, May, and Nov...
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作者：Robert, Christian P.; Inchausti, Pablo
作者单位：Universite PSL; Universite Paris-Dauphine
