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作者：Zhao, Zifeng; Ma, Ting Fung; Ng, Wai Leong; Yau, Chun Yip
作者单位：University of Notre Dame; University of South Carolina System; University of South Carolina Columbia; Hang Seng University of Hong Kong; Chinese University of Hong Kong
摘要：This article develops a unified and computationally efficient method for change-point estimation along the time dimension in a nonstationary spatio-temporal process. By modeling a nonstationary spatio-temporal process as a piecewise stationary spatio-temporal process, we consider simultaneous estimation of the number and locations of change-points, and model parameters in each segment. A composite likelihood-based criterion is developed for change-point and parameter estimation. Under the fram...
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作者：Tan, Jianbin; Liang, Decai; Guan, Yongtao; Huang, Hui
作者单位：Sun Yat Sen University; Nankai University; Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen; Renmin University of China; Renmin University of China
摘要：In this article, we consider multivariate functional time series with a two-way dependence structure: a serial dependence across time points and a graphical interaction among the multiple functions within each time point. We develop the notion of dynamic weak separability, a more general condition than those assumed in literature, and use it to characterize the two-way structure in multivariate functional time series. Based on the proposed weak separability, we develop a unified framework for ...
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作者：Cauchois, Maxime; Gupta, Suyash; Ali, Alnur; Duchi, John C.
作者单位：Stanford University; Stanford University
摘要：While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy-coming from robust statistics and optimization-is thus to build a model robust to distributional perturbations. In this article, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present ...
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作者：Linero, Antonio R.
作者单位：University of Texas System; University of Texas Austin
摘要：In problems with large amounts of missing data one must model two distinct data generating processes: the outcome process, which generates the response, and the missing data mechanism, which determines the data we observe. Under the ignorability condition of Rubin, however, likelihood-based inference for the outcome process does not depend on the missing data mechanism so that only the former needs to be estimated; partially because of this simplification, ignorability is often used as a basel...
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作者：Cai, Biao; Zhang, Jingfei; Li, Hongyu; Su, Chang; Zhao, Hongyu
作者单位：City University of Hong Kong; Emory University; Yale University; Emory University
摘要：There is a growing interest in cell-type-specific analysis from bulk samples with a mixture of different cell types. A critical first step in such analyses is the accurate estimation of cell-type proportions in a bulk sample. Although many methods have been proposed recently, quantifying the uncertainties associated with the estimated cell-type proportions has not been well studied. Lack of consideration of these uncertainties can lead to missed or false findings in downstream analyses. In thi...
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作者：Park, Kwangmoon; Keles, Sunduz
作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
摘要：Emerging single cell technologies that simultaneously capture long-range interactions of genomic loci together with their DNA methylation levels are advancing our understanding of three-dimensional genome structure and its interplay with the epigenome at the single cell level. While methods to analyze data from single cell high throughput chromatin conformation capture (scHi-C) experiments are maturing, methods that can jointly analyze multiple single cell modalities with scHi-C data are lacki...
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作者：Dorn, Jacob; Guo, Kevin
作者单位：Princeton University; Stanford University
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作者：Wang, Jianqiao; Li, Sai; Li, Hongzhe
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Renmin University of China; University of Pennsylvania
摘要：Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits, and some variants are shown to be associated with multiple complex traits. Genetic covariance between two traits is defined as the underlying covariance of genetic effects and can be used to measure the shared genetic architecture. The data used to estimate such a genetic covariance can be from the same group or different groups of individuals, and the traits can be of different...
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作者：Biswas, Niloy; Mackey, Lester
作者单位：Harvard University; Microsoft
摘要：Markov chain Monte Carlo (MCMC) provides asymptotically consistent estimates of intractable posterior expectations as the number of iterations tends to infinity. However, in large data applications, MCMC can be computationally expensive per iteration. This has catalyzed interest in approximating MCMC in a manner that improves computational speed per iteration but does not produce asymptotically consistent estimates. In this article, we propose estimators based on couplings of Markov chains to ...
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作者：Turnbull, Kathryn; Lunagomez, Simon; Nemeth, Christopher; Airoldi, Edoardo
作者单位：Lancaster University; Instituto Tecnologico Autonomo de Mexico; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：The increasing prevalence of relational data describing interactions among a target population has motivated a wide literature on statistical network analysis. In many applications, interactions may involve more than two members of the population and this data is more appropriately represented by a hypergraph. In this article, we present a model for hypergraph data that extends the well-established latent space approach for graphs and, by drawing a connection to constructs from computational t...
