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作者：Kong, Dehan
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作者：Shi, Pixu; Zhou, Yuchen; Zhang, Anru R.
作者单位：Duke University; University of Wisconsin System; University of Wisconsin Madison
摘要：In microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. We introduce a surprisingly simple, interpretable and efficient method for the estimat...
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作者：Luo, Wei; Xue, Lingzhou; Yao, Jiawei; Yu, Xiufan
作者单位：Zhejiang University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Princeton University; University of Notre Dame
摘要：We consider forecasting a single time series using a large number of predictors in the presence of a possible nonlinear forecast function. Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors to derive the reduced data for subsequent forecasting. Using directional regression and the inverse third-moment method in the stage of sufficient dimension reduction,...
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作者：Shi, H.; Drton, M.; Han, F.
作者单位：University of Washington; University of Washington Seattle; Technical University of Munich
摘要：Chatterjee (2021) introduced a simple new rank correlation coefficient that has attracted much attention recently. The coefficient has the unusual appeal that it not only estimates a population quantity first proposed by that is zero if and only if the underlying pair of random variables is independent, but also is asymptotically normal under independence. This paper compares Chatterjee's new correlation coefficient with three established rank correlations that also facilitate consistent tests...
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作者：Blanchet, Jose; Murthy, Karthyek; Si, Nian
作者单位：Stanford University; Singapore University of Technology & Design
摘要：Estimators based on Wasserstein distributionally robust optimization are obtained as solutions of min-max problems in which the statistician selects a parameter minimizing the worst-case loss among all probability models within a certain distance from the underlying empirical measure in a Wasserstein sense. While motivated by the need to identify optimal model parameters or decision choices that are robust to model misspecification, these distributionally robust estimators recover a wide range...
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作者：Wang, Jianqiao; Li, Hongzhe
作者单位：University of Pennsylvania
摘要：Genome-wide association studies have identified thousands of genetic variants that are associated with complex traits. Many complex traits are shown to share genetic etiology. Although various genetic correlation measures and their estimators have been developed, rigorous statistical analysis of their properties, including their robustness to model assumptions, is still lacking. We develop a method of moments estimator of genetic correlation between two traits in the framework of high-dimensio...
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作者：Vats, D.; Goncalves, F. B.; Latuszynski, K.; Roberts, G. O.
作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur; Universidade Federal de Minas Gerais; University of Warwick
摘要：Accept-reject-based Markov chain Monte Carlo algorithms have traditionally utilized acceptance probabilities that can be explicitly written as a function of the ratio of the target density at the two contested points. This feature is rendered almost useless in Bayesian posteriors with unknown functional forms. We introduce a new family of Markov chain Monte Carlo acceptance probabilities that has the distinguishing feature of not being a function of the ratio of the target density at the two p...
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作者：Chang, Jinyuan; Cheng, Guanghui; Yao, Qiwei
作者单位：Southwestern University of Finance & Economics - China; Guangzhou University; University of London; London School Economics & Political Science
摘要：We propose a new unit-root test for a stationary null hypothesis H-0 against a unit-root alternative H-1. Our approach is nonparametric as H-0 assumes only that the process concerned is I(0), without specifying any parametric forms. The new test is based on the fact that the sample autocovariance function converges to the finite population autocovariance function for an I(0) process, but diverges to infinity for a process with unit roots. Therefore, the new test rejects H-0 for large values of...
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作者：Zhang, Yuqian; Bradic, Jelena
作者单位：Renmin University of China; University of California System; University of California San Diego
摘要：Afundamental challenge in semi-supervised learning lies in the observed data's disproportional size when compared with the size of the data collected with missing outcomes. An implicit understanding is that the dataset with missing outcomes, being significantly larger, ought to improve estimation and inference. However, it is unclear to what extent this is correct. We illustrate one clear benefit: root-n inference of the outcome's mean is possible while only requiring a consistent estimation o...
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作者：Guinness, Joseph
作者单位：Cornell University
摘要：We conduct a study of the aliased spectral densities of Matern covariance functions on a regular grid of points, elucidating the properties of a popular approximation based on stochastic partial differential equations. While other researchers have shown that this approximation can work well for the covariance function, we find that it assigns too much power at high frequencies and does not provide increasingly accurate approximations to the inverse as the grid spacing goes to zero, except in t...
