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作者：Park, Seyoung; Lee, Eun Ryung; Zhao, Hongyu
作者单位：Sungkyunkwan University (SKKU); Yale University
摘要：In this article, we study high-dimensional multivariate logistic regression models in which a common set of covariates is used to predict multiple binary outcomes simultaneously. Our work is primarily motivated from many biomedical studies with correlated multiple responses such as the cancer cell-line encyclopedia project. We assume that the underlying regression coefficient matrix is simultaneously low-rank and row-wise sparse. We propose an intuitively appealing selection and estimation fra...
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作者：Cai, Biao; Zhang, Jingfei; Guan, Yongtao
作者单位：University of Miami
摘要：Learning the latent network structure from large scale multivariate point process data is an important task in a wide range of scientific and business applications. For instance, we might wish to estimate the neuronal functional connectivity network based on spiking times recorded from a collection of neurons. To characterize the complex processes underlying the observed data, we propose a new and flexible class of nonstationary Hawkes processes that allow both excitatory and inhibitory effect...
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作者：Morales-Navarrete, Diego; Bevilacqua, Moreno; Caamano-Carrillo, Christian; Castro, Luis M.
作者单位：Pontificia Universidad Catolica de Chile; Universidad Adolfo Ibanez; Universita Ca Foscari Venezia; Universidad del Bio-Bio; Pontificia Universidad Catolica de Chile
摘要：Random fields are useful mathematical tools for representing natural phenomena with complex dependence structures in space and/or time. In particular, the Gaussian random field is commonly used due to its attractive properties and mathematical tractability. However, this assumption seems to be restrictive when dealing with counting data. To deal with this situation, we propose a random field with a Poisson marginal distribution considering a sequence of independent copies of a random field wit...
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作者：Liu, Wanjun; Yu, Xiufan; Zhong, Wei; Li, Runze
作者单位：University of Notre Dame; Xiamen University; Xiamen University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：This article studies the projection test for high-dimensional mean vectors via optimal projection. The idea of projection test is to project high-dimensional data onto a space of low dimension such that traditional methods can be applied. We first propose a new estimation for the optimal projection direction by solving a constrained and regularized quadratic programming. Then two tests are constructed using the estimated optimal projection direction. The first one is based on a data-splitting ...
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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：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...
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作者：Miao, Zhen; Kong, Weihao; Vinayak, Ramya Korlakai; Sun, Wei; Han, Fang
作者单位：University of Washington; University of Washington Seattle; Alphabet Inc.; Google Incorporated; University of Wisconsin System; University of Wisconsin Madison; Fred Hutchinson Cancer Center
摘要：This article investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions. Building on nonparametric maximum likelihood estimators (NPMLEs) of the mixing distribution, these tests are theoretically shown to be able to adapt to complicated unspecified structures of count data and also consistent against their corresponding ANOVA-type alternatives; the latter is a result in parallel to classic c...
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作者：Fan, Jianqing; Guo, Yongyi; Yu, Mengxin
作者单位：Princeton University
摘要：In this article, we study the contextual dynamic pricing problem where the market value of a product is linear in its observed features plus some market noise. Products are sold one at a time, and only a binary response indicating success or failure of a sale is observed. Our model setting is similar to the work by Javanmard and Nazerzadeh except that we expand the demand curve to a semiparametric model and learn dynamically both parametric and nonparametric components. We propose a dynamic st...
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作者：Tao, Jun; Li, Bing; Xue, Lingzhou
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence. Additive conditional independence is a three-way statistical relation that shares similar properties with conditional independence by satisfying the semi-graphoid axioms. Based on this relation we build an additive graphical model for discrete variables that does not suffer from the restriction of a parametric model such as the Ising model. We develop an estimator of the new gra...
