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作者：Wang, Fan; Li, Wanshan; Madrid Padilla, Oscar Hernan; Yu, Yi; Rinaldo, Alessandro
作者单位：University of Warwick; University of California System; University of California Los Angeles; University of Texas System; University of Texas Austin
摘要：We study the multilayer random dot product graph (MRDPG) model, a generalization of the random dot product graph model to multilayer networks. To estimate the edge probabilities, we deploy a tensor-based methodology and demonstrate its superiority over existing approaches. Moving to dynamic MRDPGs, we formulate and analyse an online change point detection framework, where, at each time point, we observe a realization from an MRDPG. Across layers, we assume fixed shared common node sets and lat...
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作者：Yang, Shu; Ding, Peng
作者单位：North Carolina State University; University of California System; University of California Berkeley
摘要：Rejective sampling improves design and estimation efficiency of single-phase sampling when auxiliary information in a finite population is available. When such auxiliary information is unavailable, we propose to use two-phase rejective sampling (TPRS), which involves measuring auxiliary variables for the sample of units in the first phase, followed by the implementation of rejective sampling for the outcome in the second phase. We explore the asymptotic design properties of double expansion an...
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作者：Tian, Ye; Xu, Hongquan
作者单位：Beijing University of Posts & Telecommunications; University of California System; University of California Los Angeles
摘要：Space-filling designs are widely used in computer experiments. We propose a stratified L2-discrepancy to evaluate the uniformity of a design when the design domain is stratified into various subregions. Weights are used to adjust preferences for the uniformity over subregions in each stratification. The stratified L2-discrepancy is easy to compute, satisfies a Koksma-Hlawka type inequality, and overcomes the curse of dimensionality that exists for other discrepancies. It is applicable to a bro...
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作者：Lyu, Zhongyuan; Xia, Dong
作者单位：Hong Kong University of Science & Technology
摘要：This article investigates the computational and statistical limits in clustering matrix-valued observations. We propose a low-rank mixture model (LrMM), adapted from the classical Gaussian mixture model (GMM), to handle matrix-valued observations, assuming low-rankness for population centre matrices. A computationally efficient clustering method is designed by integrating Lloyd's algorithm and low-rank approximation. Once well-initialized, the algorithm converges fast and achieves an exponenti...
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作者：Chen, Haolin; Dette, Holger; Yu, Jun
作者单位：Beijing Institute of Technology; Ruhr University Bochum
摘要：Subsampling is one of the popular methods to balance statistical efficiency and computational efficiency in the big data era. Most approaches aim to select informative or representative sample points to achieve good overall information of the full data. The present work takes the view that sampling techniques are recommended for the region we focus on and summary measures are enough to collect the information for the rest according to a well-designed data partitioning. We propose a subsampling...
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作者：Jacobson, Tate
作者单位：Oregon State University
摘要：Partial penalized tests provide flexible approaches to testing linear hypotheses in high-dimensional generalized linear models. However, because the estimators used in these tests are local minimizers of potentially nonconvex folded-concave penalized objectives, the solutions one computes in practice may not coincide with the unknown local minima for which we have nice theoretical guarantees. To close this gap between theory and computation, we introduce local linear approximation (LLA) algori...
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作者：Papp, Tamas P.; Sherlock, Chris
作者单位：Lancaster University; Lancaster University
摘要：There has been a recent surge of interest in coupling methods for Markov chain Monte Carlo algorithms: they facilitate convergence quantification and unbiased estimation, while exploiting embarrassingly parallel computing capabilities. Motivated by these, we consider the design and analysis of couplings of the random walk Metropolis algorithm which scale well with the dimension of the target measure. Methodologically, we introduce a low-rank modification of the synchronous coupling that is pro...
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作者：Borgonovo, Emanuele; Figalli, Alessio; Ghosal, Promit; Plischke, Elmar; Savare, Giuseppe
作者单位：Bocconi University; Bocconi University; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Chicago; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
摘要：Recent investigations on the measures of statistical association highlight essential properties such as zero-independence (the measure is zero if and only if the random variables are independent), monotonicity under information refinement, and max-functionality (the measure of association is maximal if and only if we are in the presence of a deterministic (noiseless) dependence). An open question concerns the reasons why measures of statistical associations satisfy one or more of those propert...
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作者：Tan, Linda S. L.
作者单位：National University of Singapore
摘要：Natural gradients can improve convergence in stochastic variational inference significantly but inverting the Fisher information matrix is daunting in high dimensions. Moreover, in Gaussian variational approximation, natural gradient updates of the precision matrix do not ensure positive definiteness. To tackle this issue, we derive analytic natural gradient updates of the Cholesky factor of the covariance or precision matrix and consider sparsity constraints representing different posterior c...
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作者：Bong, Heejong; Ventura, Valerie; Wasserman, Larry
作者单位：University of Michigan System; University of Michigan; Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
摘要：The effect of public health interventions on an epidemic are often estimated by adding the intervention to epidemic models. During the Covid-19 epidemic, numerous papers used such methods for making scenario predictions. The majority of these papers use Bayesian methods to estimate the parameters of the model. In this article, we show how to use frequentist methods for estimating these effects which avoids having to specify prior distributions. We also use model-free shrinkage methods to impro...
