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作者：Deng, Siyi; Ning, Yang; Zhao, Jiwei; Zhang, Heping
作者单位：Cornell University; University of Wisconsin System; University of Wisconsin Madison; Yale University
摘要：We consider the estimation problem in high-dimensional semi-supervised learning. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis. We first establish the minimax lower bound for parameter estimation in the semi-supervised setting, and show that this lower bound cannot be achieved by supervised estimators using the la...
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作者：Zens, Gregor; Fruhwirth-Schnatter, Sylvia; Wagner, Helga
作者单位：International Institute for Applied Systems Analysis (IIASA); Johannes Kepler University Linz
摘要：Modeling binary and categorical data is one of the most commonly encountered tasks of applied statisticians and econometricians. While Bayesian methods in this context have been available for decades now, they often require a high level of familiarity with Bayesian statistics or suffer from issues such as low sampling efficiency. To contribute to the accessibility of Bayesian models for binary and categorical data, we introduce novel latent variable representations based on Polya-Gamma random ...
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作者：Zhang, Xiran; Salvana, Mary Lai O.; Lenzi, Amanda; Genton, Marc G.
作者单位：King Abdullah University of Science & Technology; University of Connecticut; University of Edinburgh
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作者：Zhu, Qiuyun; Atchade, Yves
作者单位：Boston University; University of Minnesota System; University of Minnesota Twin Cities
摘要：Canonical correlation analysis (CCA) is a popular statistical technique for exploring relationships between datasets. In recent years, the estimation of sparse canonical vectors has emerged as an important but challenging variant of the CCA problem, with widespread applications. Unfortunately, existing rate-optimal estimators for sparse canonical vectors have high computational cost. We propose a quasi-Bayesian estimation procedure that not only achieves the minimax estimation rate, but also i...
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作者：Lim, Chae Young
作者单位：Seoul National University (SNU)
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作者：Zhang, Qi; Xue, Lingzhou; Li, Bing
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：With the rapid development of data collection techniques, complex data objects that are not in the Euclidean space are frequently encountered in new statistical applications. Frechet regression model (Petersen and Muller) provides a promising framework for regression analysis with metric space-valued responses. In this article, we introduce a flexible sufficient dimension reduction (SDR) method for Frechet regression to achieve two purposes: to mitigate the curse of dimensionality caused by hi...
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作者：He, Qijia; Gao, Fei; Dukes, Oliver; Delany-Moretlwe, Sinead; Zhang, Bo
作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; Ghent University; University of Witwatersrand
摘要：In many clinical settings, an active-controlled trial design (e.g., a non-inferiority or superiority design) is often used to compare an experimental medicine to an active control (e.g., an FDA-approved, standard therapy). One prominent example is a recent phase 3 efficacy trial, HIV Prevention Trials Network Study 084 (HPTN 084), comparing long-acting cabotegravir, a new HIV pre-exposure prophylaxis (PrEP) agent, to the FDA-approved daily oral tenofovir disoproxil fumarate plus emtricitabine ...
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作者：Li, Meng; Liu, Zejian; Yu, Cheng-Han; Vannucci, Marina
作者单位：Rice University; Marquette University
摘要：There is a wide range of applications where the local extrema of a function are the key quantity of interest. However, there is surprisingly little work on methods to infer local extrema with uncertainty quantification in the presence of noise. By viewing the function as an infinite-dimensional nuisance parameter, a semiparametric formulation of this problem poses daunting challenges, both methodologically and theoretically, as (i) the number of local extrema may be unknown, and (ii) the induc...
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作者：Alonso-Pena, Maria; Gijbels, Irene; Crujeiras, Rosa M.
作者单位：KU Leuven; Universidade de Santiago de Compostela; KU Leuven; KU Leuven
摘要：This article presents a general framework for the estimation of regression models with circular covariates, where the conditional distribution of the response given the covariate can be specified through a parametric model. The estimation of a conditional characteristic is carried out nonparametrically, by maximizing the circular local likelihood, and the estimator is shown to be asymptotically normal. The problem of selecting the smoothing parameter is also addressed, as well as bias and vari...
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作者：Raymaekers, Jakob; Rousseeuw, Peter J.
作者单位：Maastricht University; KU Leuven
摘要：The usual Minimum Covariance Determinant (MCD) estimator of a covariance matrix is robust against casewise outliers. These are cases (that is, rows of the data matrix) that behave differently from the majority of cases, raising suspicion that they might belong to a different population. On the other hand, cellwise outliers are individual cells in the data matrix. When a row contains one or more outlying cells, the other cells in the same row still contain useful information that we wish to pre...
