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作者：Barigozzi, Matteo; Farne, Matteo
作者单位：University of Bologna; University of Bologna
摘要：We propose a new estimator of high-dimensional spectral density matrices, called ALgebraic Spectral Estimator (ALSE), under the assumption of an underlying low rank plus sparse structure, as typically assumed in dynamic factor models. The ALSE is computed by minimizing a quadratic loss under a nuclear norm plus ti norm constraint to control the latent rank and the residual sparsity pattern. The loss function requires as input the classical smoothed periodogram estimator and two threshold param...
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作者：Zhao, Anqi; Ding, Peng
作者单位：National University of Singapore; University of California System; University of California Berkeley
摘要：Randomized experiments allow for consistent estimation of the average treatment effect based on the difference in mean outcomes without strong modeling assumptions. Appropriate use of pretreatment covariates can further improve the estimation efficiency. Missingness in covariates is nevertheless common in practice, and raises an important question: should we adjust for covariates subject to missingness, and if so, how? The unadjusted difference in means is always unbiased. The complete-covaria...
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作者：Dorn, Jacob; Guo, Kevin
作者单位：Princeton University; Stanford University
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作者：Shi, Chengchun; Qi, Zhengling; Wang, Jianing; Zhou, Fan
作者单位：University of London; London School Economics & Political Science; George Washington University; Shanghai University of Finance & Economics
摘要：Reinforcement learning (RL) is a powerful machine learning technique that enables an intelligent agent to learn an optimal policy that maximizes the cumulative rewards in sequential decision making. Most of methods in the existing literature are developed in online settings where the data are easy to collect or simulate. Motivated by high stake domains such as mobile health studies with limited and pre-collected data, in this article, we study offline reinforcement learning methods. To efficie...
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作者：Almendra-Hernandez, Felix; De Loera, Jesus A.; Petrovic, Sonja
作者单位：University of California System; University of California Davis; Illinois Institute of Technology
摘要：In this article, we evaluate the challenges and best practices associated with the Markov bases approach to sampling from conditional distributions. We provide insights and clarifications after 25 years of the publication of the Fundamental theorem for Markov bases by Diaconis and Sturmfels. In addition to a literature review, we prove three new results on the complexity of Markov bases in hierarchical models, relaxations of the fibers in log-linear models, and limitations of partial sets of m...
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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：Matsushita, Yukitoshi; Otsu, Taisuke
作者单位：Hitotsubashi University; University of London; London School Economics & Political Science
摘要：This article develops a concept of nonparametric likelihood for network data based on network moments, and proposes general inference methods by adapting the theory of jackknife empirical likelihood. Our methodology can be used not only to conduct inference on population network moments and parameters in network formation models, but also to implement goodness-of-fit testing, such as testing block size for stochastic block models. Theoretically we show that the jackknife empirical likelihood s...
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作者：Fan, Jianqing; Lou, Zhipeng; Yu, Mengxin
作者单位：Fudan University; Princeton University; Princeton University
摘要：We propose the Factor Augmented (sparse linear) Regression Model (FARM) that not only admits both the latent factor regression and sparse linear regression as special cases but also bridges dimension reduction and sparse regression together. We provide theoretical guarantees for the estimation of our model under the existence of sub-Gaussian and heavy-tailed noises (with bounded (1+theta) th moment, for all theta > 0), respectively. In addition, the existing works on supervised learning often ...
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作者：Viviano, Davide; Bradic, Jelena
作者单位：Stanford University; Harvard University; University of California System; University of California San Diego; University of California System; University of California San Diego
摘要：One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination: individualized treatments may induce disparities across sensitive attributes such as age, gender, or race. This article addresses the question of the design of fair and efficient treatment allocation rules. We adopt the nonmaleficence perspective of first do no harm : we select the fairest allocation within the Pareto frontier. We cast the optimization into a mixed-integer linear p...
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作者：Zhou, Shuang; Ray, Pallavi; Pati, Debdeep; Bhattacharya, Anirban
作者单位：Arizona State University; Arizona State University-Tempe; Eli Lilly; Lilly Research Laboratories; Texas A&M University System; Texas A&M University College Station
摘要：We show that lower-dimensional marginal densities of dependent zero-mean normal distributions truncated to the positive orthant exhibit a mass-shifting phenomenon. Despite the truncated multivariate normal density having a mode at the origin, the marginal density assigns increasingly small mass near the origin as the dimension increases. The phenomenon accentuates with stronger correlation between the random variables. This surprising behavior has serious implications toward Bayesian constrain...
