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作者：Ma, Xin; Kundu, Suprateek
作者单位：Emory University; University of Texas System; UTMD Anderson Cancer Center
摘要：Recent medical imaging studies have given rise to distinct but inter-related datasets corresponding to multiple experimental tasks or longitudinal visits. Standard scalar-on-image regression models that fit each dataset separately are not equipped to leverage information across inter-related images, and existing multi-task learning approaches are compromised by the inability to account for the noise that is often observed in images. We propose a novel joint scalar-on-image regression framework...
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作者：Mastrototaro, Alessandro; Olsson, Jimmy; Alenlov, Johan
作者单位：Royal Institute of Technology; Linkoping University
摘要：We present a novel sequential Monte Carlo approach to online smoothing of additive functionals in a very general class of path-space models. Hitherto, the solutions proposed in the literature suffer from either long-term numerical instability due to particle-path degeneracy or, in the case that degeneracy is remedied by particle approximation of the so-called backward kernel, high computational demands. In order to balance optimally computational speed against numerical stability, we propose t...
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作者：Chong, Carsten H.; Delerue, Thomas; Li, Guoying
作者单位：Hong Kong University of Science & Technology; Columbia University
摘要：The analysis of high-frequency financial data is often impeded by the presence of noise. This article is motivated by intraday return data in which market microstructure noise appears to be rough, that is, best captured by a continuous-time stochastic process that locally behaves as fractional Brownian motion. Assuming that the underlying efficient price process follows a continuous It & ocirc; semimartingale, we derive consistent estimators and asymptotic confidence intervals for the roughnes...
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作者：Zhang, Xiran; Salvana, Mary Lai O.; Lenzi, Amanda; Genton, Marc G.
作者单位：King Abdullah University of Science & Technology; University of Connecticut; University of Edinburgh
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作者：Zhu, Qiuyun; Atchade, Yves
作者单位：Boston University; University of Minnesota System; University of Minnesota Twin Cities
摘要：Canonical correlation analysis (CCA) is a popular statistical technique for exploring relationships between datasets. In recent years, the estimation of sparse canonical vectors has emerged as an important but challenging variant of the CCA problem, with widespread applications. Unfortunately, existing rate-optimal estimators for sparse canonical vectors have high computational cost. We propose a quasi-Bayesian estimation procedure that not only achieves the minimax estimation rate, but also i...
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作者：Wang, Yixin; Degleris, Anthony; Williams, Alex; Linderman, Scott W.
作者单位：University of Michigan System; University of Michigan; Stanford University; New York University; Simons Foundation; Flatiron Institute; Stanford University; Stanford University
摘要：Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural spike trains to document streams. The clustering property is achieved via a doubly stochastic formulation: first, a set of latent events is drawn from a Poisson process; then, each latent event generates a set of observed data points according to another Poisson process. This construction is similar to Bayesian nonp...
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作者：Hwang, Neil; Xu, Jiarui; Chatterjee, Shirshendu; Bhattacharyya, Sharmodeep
作者单位：City University of New York (CUNY) System; City University of New York (CUNY) System; Oregon State University
摘要：Among the nonparametric methods of estimating the number of communities (K) in a community detection problem, methods based on the spectrum of the Bethe Hessian matrices (H-? with the scalar parameter ?) have garnered much popularity for their simplicity, computational efficiency, and robustness to the sparsity of data. For certain heuristic choices of ?, such methods have been shown to be consistent for networks with N nodes with a common expected degree of ?( log N). In this article, we obta...
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作者：Fang, Qin; Guo, Shaojun; Qiao, Xinghao
作者单位：University of London; London School Economics & Political Science; Renmin University of China
摘要：Covariance function estimation is a fundamental task in multivariate functional data analysis and arises in many applications. In this paper, we consider estimating sparse covariance functions for high-dimensional functional data, where the number of random functions p is comparable to, or even larger than the sample size n. Aided by the Hilbert--Schmidt norm of functions, we introduce a new class of functional thresholding operators that combine functional versions of thresholding and shrinka...
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作者：Lijoi, Antonio; Pruenster, Igor; Rigon, Tommaso
作者单位：Bocconi University; University of Milano-Bicocca; Bocconi University
摘要：Discrete random probability measures stand out as effective tools for Bayesian clustering. The investigation in the area has been very lively, with a strong emphasis on nonparametric procedures based on either the Dirichlet process or on more flexible generalizations, such as the normalized random measures with independent increments (NRMI). The literature on finite-dimensional discrete priors is much more limited and mostly confined to the standard Dirichlet-multinomial model. While such a sp...
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作者：Zhang, Xin; Liu, Jia; Zhu, Zhengyuan
作者单位：Iowa State University; University System of Ohio; Ohio State University
摘要：Identifying the latent cluster structure based on model heterogeneity is a fundamental but challenging task arises in many machine learning applications. In this article, we study the clustered coefficient regression problem in the distributed network systems, where the data are locally collected and held by nodes. Our work aims to improve the regression estimation efficiency by aggregating the neighbors' information while also identifying the cluster membership for nodes. To achieve efficient...
