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作者：Billio, Monica; Casarin, Roberto; Iacopini, Matteo
作者单位：Universita Ca Foscari Venezia; Vrije Universiteit Amsterdam
摘要：Modeling time series of multilayer network data is challenging due to the peculiar characteristics of real-world networks, such as sparsity and abrupt structural changes. Moreover, the impact of external factors on the network edges is highly heterogeneous due to edge- and time-specific effects. Capturing all these features results in a very high-dimensional inference problem. A novel tensor-on-tensor regression model is proposed, which integrates zero-inflated logistic regression to deal with...
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作者：Qiu, Jiaming; Dai, Xiongtao; Zhu, Zhengyuan
作者单位：Iowa State University; University of California System; University of California Berkeley
摘要：We consider the estimation of densities in multiple subpopulations, where the available sample size in each subpopulation greatly varies. This problem occurs in epidemiology, for example, where different diseases may share similar pathogenic mechanism but differ in their prevalence. Without specifying a parametric form, our proposed method pools information from the population and estimate the density in each subpopulation in a data-driven fashion. Drawing from functional data analysis, low-di...
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作者：Qin, Qian
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：This article studies the convergence properties of trans-dimensional MCMC algorithms when the total number of models is finite. It is shown that, for reversible and some nonreversible trans-dimensional Markov chains, under mild conditions, geometric convergence is guaranteed if the Markov chains associated with the within-model moves are geometrically ergodic. This result is proved in an L2 framework using the technique of Markov chain decomposition. While the technique was previously develope...
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作者：Zhao, Bingxin; Yang, Xiaochen; Zhu, Hongtu
作者单位：University of Pennsylvania; Purdue University System; Purdue University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：The aim of this article is to propose a novel method for estimating trans-ancestry genetic correlations in genome-wide association studies (GWAS) using genetically predicted observations. These correlations describe how genetic architecture of complex traits varies among populations. Our new estimator corrects for biases arising from prediction errors in high-dimensional weak GWAS signals, while addressing the ethnic diversity inherent in GWAS data, such as linkage disequilibrium (LD) differen...
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作者：Bortolotti, Teresa; Peli, Riccardo; Lanzano, Giovanni; Sgobba, Sara; Menafoglio, Alessandra
作者单位：Polytechnic University of Milan; Istituto Nazionale Geofisica e Vulcanologia (INGV)
摘要：Motivated by the crucial implications of Ground Motion Models in terms of seismic hazard analysis and civil protection planning, this work extends a scalar Ground Motion Model for Italy to the framework of Functional Data Analysis. The inherent characteristic of seismic data to be incomplete over the observation domain of oscillation periods entails embedding the analysis in the context of partially observed functional data and performing data reconstruction. This work proposes a novel methodo...
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作者：Ye, Ting; Brumback, Babette A.
作者单位：University of Washington; University of Washington Seattle
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作者：Zhong, Wei; Li, Zhuoxi; Guo, Wenwen; Cui, Hengjian
作者单位：Xiamen University; Xiamen University; Capital Normal University
摘要：We propose a new measure of dependence between a categorical random variable and a random vector with potentially high dimensions, named semi-distance correlation. It is an interesting extension of distance correlation to accommodate the information of the categorical random variable. It equals zero if and only if the categorical random variable and the other random vector are independent. Two important applications of semi-distance correlation are considered. First, we develop a semi-distance...
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作者：Zimmerman, Robert; Craiu, Radu V.; Leos-Barajas, Vianey
作者单位：University of Toronto; University of Toronto
摘要：We propose a copula-based extension of the hidden Markov model (HMM) which applies when the observations recorded at each time in the sample are multivariate. The joint model produced by the copula extension allows decoding of the hidden states based on information from multiple observations. However, unlike the case of independent marginals, the copula dependence structure embedded into the likelihood poses additional computational challenges. We tackle the latter using a theoretically-justif...
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作者：Ki, Caleb; Terhorst, Jonathan
作者单位：University of Michigan System; University of Michigan
摘要：In statistical genetics, the sequentially Markov coalescent (SMC) is an important family of models for approximating the distribution of genetic variation data under complex evolutionary models. Methods based on SMC are widely used in genetics and evolutionary biology, with significant applications to genotype phasing and imputation, recombination rate estimation, and inferring population history. SMC allows for likelihood-based inference using hidden Markov models (HMMs), where the latent var...
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作者：Rao, J. Sunil; Li, Mengying; Jiang, Jiming
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Miami; University of California System; University of California Davis
摘要：In many practical problems, there is interest in the estimation of mixed effect projections for new data that are outside the range of the training data. Examples include predicting extreme small area means for rare populations or making treatment decisions for patients who do not fit typical risk profiles. Standard methods have long been known to struggle with such problems since the training data may not provide enough information about potential model changes for these new data values (extr...
