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作者：Aue, Alexander
作者单位：University of California System; University of California Davis
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作者：Tuvaandorj, Purevdorj
作者单位：York University - Canada
摘要：This article develops permutation versions of identification-robust tests in linear instrumental variables regression. Unlike the existing randomization and rank-based tests in which independence between the instruments and the error terms is assumed, the permutation Anderson-Rubin (AR), Lagrange Multiplier (LM) and Conditional Likelihood Ratio (CLR) tests are asymptotically similar and robust to conditional heteroscedasticity under standard exclusion restriction, that is, the orthogonality be...
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作者：Shao, Simeng; Bien, Jacob; Javanmard, Adel
作者单位：Amazon.com; University of Southern California
摘要：In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements. For example, companies belong to industries, which in turn belong to ever coarser divisions such as sectors; microbes are commonly arranged in a taxonomic hierarchy from species to kingdoms; street blocks belong to neighborhoods, which in turn belong to larger-scale regions. The problem of tree-based aggregation that we consider in this article asks w...
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作者：Kwon, Oh-Ran; Zou, Hui
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Southern California
摘要：The response envelope model provides substantial efficiency gains over the standard multivariate linear regression by identifying the material part of the response to the model and by excluding the immaterial part. In this article, we propose the enhanced response envelope by incorporating a novel envelope regularization term based on a nonconvex manifold formulation. It is shown that the enhanced response envelope can yield better prediction risk than the original envelope estimator. The enha...
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作者：Jin, Jiashun; Ke, Zheng Tracy; Luo, Shengming; Ma, Yucong
作者单位：Carnegie Mellon University; Harvard University
摘要：We are interested in the problem of two-sample network hypothesis testing: given two networks with the same set of nodes, we wish to test whether the underlying Bernoulli probability matrices of the two networks are the same or not. We propose Interlacing Balance Measure (IBM) as a new two-sample testing approach. We consider the Degree-Corrected Mixed-Membership (DCMM) model for undirected networks, where we allow severe degree heterogeneity, mixed-memberships, flexible sparsity levels, and w...
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作者：Nishimura, Akihiko; Zhang, Zhenyu; Suchard, Marc A.
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
摘要：Zigzag and other piecewise deterministic Markov process samplers have attracted significant interest for their non-reversibility and other appealing properties for Bayesian posterior computation. Hamiltonian Monte Carlo is another state-of-the-art sampler, exploiting fictitious momentum to guide Markov chains through complex target distributions. We establish an important connection between the zigzag sampler and a variant of Hamiltonian Monte Carlo based on Laplace-distributed momentum. The p...
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作者：Stein, Stefan; Feng, Rui; Leng, Chenlei
作者单位：University of Warwick
摘要：For statistical analysis of network data, the beta -model has emerged as a useful tool, thanks to its flexibility in incorporating nodewise heterogeneity and theoretical tractability. To generalize the beta -model, this article proposes the Sparse beta -Regression Model (S beta RM) that unites two research themes developed recently in modeling homophily and sparsity. In particular, we employ differential heterogeneity that assigns weights only to important nodes and propose penalized likelihoo...
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作者：Cai, Leheng; Guo, Xu; Zhong, Wei
作者单位：Tsinghua University; Tsinghua University; Beijing Normal University; Xiamen University; Xiamen University
摘要：It is of importance to investigate the significance of a subset of covariates W for the response Y given covariates Z in regression modeling. To this end, we propose a significance test for the partial mean independence problem based on machine learning methods and data splitting. The test statistic converges to the standard Chi-squared distribution under the null hypothesis while it converges to a normal distribution under the fixed alternative hypothesis. Power enhancement and algorithm stab...
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作者：Zhang, Shuangjie; Shen, Yuning; Chen, Irene A.; Lee, Juhee
作者单位：University of California System; University of California Santa Cruz; University of California System; University of California Los Angeles
摘要：Group factor models have been developed to infer relationships between multiple co-occurring multivariate continuous responses. Motivated by complex count data from multi-domain microbiome studies using next-generation sequencing, we develop a sparse Bayesian group factor model (Sp-BGFM) for multiple count table data that captures the interaction between microorganisms in different domains. Sp-BGFM uses a rounded kernel mixture model using a Dirichlet process (DP) prior with log-normal mixture...
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作者：Qiu, Yixuan; Gao, Qingyi; Wang, Xiao
作者单位：Shanghai University of Finance & Economics; Purdue University System; Purdue University
摘要：Generative models based on latent variables, such as generative adversarial networks (GANs) and variational auto-encoders (VAEs), have gained lots of interests due to their impressive performance in many fields. However, many data such as natural images usually do not populate the ambient Euclidean space but instead reside in a lower-dimensional manifold. Thus an inappropriate choice of the latent dimension fails to uncover the structure of the data, possibly resulting in mismatch of latent re...
