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作者：Meng, Xuran; Cao, Yuan; Wang, Weichen
作者单位：University of Michigan System; University of Michigan; University of Hong Kong
摘要：Portfolio optimization aims at constructing a realistic portfolio with significant out-of-sample performance, which is typically measured by the out-of-sample Sharpe ratio. However, due to in-sample optimism, it is inappropriate to use the in-sample estimated covariance to evaluate the out-of-sample Sharpe, especially in the high dimensional settings. In this article, we propose a novel method to estimate the out-of-sample Sharpe ratio using only in-sample data, based on random matrix theory. ...
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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：Lee, Chanhwa; Zeng, Donglin; Hudgens, Michael G.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Michigan System; University of Michigan
摘要：Interference occurs when a unit's treatment (or exposure) affects another unit's outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the same cluster, that is, there is clustered interference. Various causal estimands have been proposed to quantify treatment effects under clustered interference from observational data, but these estimands either entail treatment policies lacking rea...
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作者：Luo, Tianpai; Wu, Weichi
作者单位：Tsinghua University
摘要：We propose a new framework for the simultaneous inference of monotone and smoothly time-varying functions under complex temporal dynamics. This will be done using the monotone rearrangement and the nonparametric estimation. We capitalize the Gaussian approximation for the nonparametric monotone estimator and construct the asymptotically correct simultaneous confidence bands (SCBs) using designed bootstrap methods. We investigate two general and practical scenarios. The first is the simultaneou...
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作者：Zhang, Zhe; Yu, Xiufan; Li, Runze
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Notre Dame
摘要：This article proposes an innovative double power-enhanced testing procedure for inference on high-dimensional linear hypotheses in high-dimensional regression models. Through a projection approach that aims to separate useful inferential information from the nuisance one, our proposed test accurately accounts for the impact of high-dimensional nuisance parameters. We discover that with a carefully-designed projection matrix, the projection procedure enables us to transform the problem of inter...
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作者：Guo, Zijian; Li, Xiudi; Han, Larry; Cai, Tianxi
作者单位：Rutgers University System; Rutgers University New Brunswick; University of California System; University of California Berkeley; Northeastern University; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School
摘要：Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogeneity across sources and data-sharing constraints. In this article, we consider a general robust inference framework for federated meta-learning of data from multiple sites, enabling statistical inference for the prevailing model, defined as the one matching the majority of the sites. Statistical inference for the prev...
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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...
