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作者：Yang, Tao; Huang, Ying; Fong, Youyi
作者单位：Fred Hutchinson Cancer Center
摘要：We consider the use of threshold-based regression models to evaluate immune response biomarkers as principal surrogate markers of a vaccine's protective effect. Threshold-based regression models, which allow the relationship between a clinical outcome and a covariate to change dramatically across a threshold value in the covariate, have been studied by various authors under fully observed data. Limited research, however, has examined these models in the presence of missing covariates, such as ...
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作者：Van den Boom, W.; Reeves, G.; Dunson, D. B.
作者单位：Yale NUS College; National University of Singapore; Duke University
摘要：Posterior computation for high-dimensional data with many parameters can be challenging. This article focuses on a new method for approximating posterior distributions of a low- to moderate-dimensional parameter in the presence of a high-dimensional or otherwise computationally challenging nuisance parameter. The focus is on regression models and the key idea is to separate the likelihood into two components through a rotation. One component involves only the nuisance parameters, which can the...
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作者：Zheng, Yao; Cheng, Guang
作者单位：University of Connecticut; Purdue University System; Purdue University
摘要：This paper develops a unified finite-time theory for the ordinary least squares estimation of possibly unstable and even slightly explosive vector autoregressive models under linear restrictions, with the applicable region rho(A) <= 1 + c/n, where rho(A) is the spectral radius of the transition matrix A in the VAR(1) representation, n is the time horizon and c > 0 is a universal constant. The linear restriction framework encompasses various existing models such as banded/network vector autoreg...
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作者：Guo, F. Richard; Richardson, Thomas S.; Robins, James M.
作者单位：University of Washington; University of Washington Seattle; Harvard University; Harvard T.H. Chan School of Public Health
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作者：Jeong, Seonghyun; Ghosal, Subhashis
作者单位：Yonsei University; North Carolina State University
摘要：We study posterior contraction rates in sparse high-dimensional generalized linear models using priors incorporating sparsity. A mixture of a point mass at zero and a continuous distribution is used as the prior distribution on regression coefficients. In addition to the usual posterior, the fractional posterior, which is obtained by applying Bayes theorem with a fractional power of the likelihood, is also considered. The latter allows uniformity in posterior contraction over a larger subset o...
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作者：Biscio, C. A. N.; Moller, J.
作者单位：Aalborg University
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作者：Kim, S.; Cho, H.; Bang, D.; De Marchi, D.; El-Zaatari, H.; Shah, K. S.; Valancius, M.; Zikry, T. M.; Kosorok, M. R.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：In this discussion, we examine the contributions of Qian et al. (2021) and potential applications of the newly developed estimator for the causal excursion effect in binary outcome data. Specifically, we consider extension of their method to count outcomes and observational data, propose an alternative use of their method for analysing excursion effect trajectories and discuss ways of improving estimator efficiency.
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作者：Deresa, N. W.; Van Keilegom, I
作者单位：KU Leuven
摘要：When modelling survival data, it is common to assume that the survival time T is conditionally independent of the censoring time C given a set of covariates. However, there are numerous situations in which this assumption is not realistic. The goal of this paper is therefore to develop a semiparametric normal transformation model which assumes that, after a proper nonparametric monotone transformation, the vector (T, C) follows a linear model, and the vector of errors in this bivariate linear ...
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作者：Chung, Moo K.; Ombao, Hernando
作者单位：University of Wisconsin System; University of Wisconsin Madison; King Abdullah University of Science & Technology
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作者：Zhang, Weiping; Jin, Baisuo; Bai, Zhidong
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Northeast Normal University - China
摘要：We introduce a conceptually simple, efficient and easily implemented approach for learning the block structure in a large matrix. Using the properties of U-statistics and large-dimensional random matrix theory, the group structure of many variables can be directly identified based on the eigenvalues and eigenvectors of the scaled sample matrix. We also establish the asymptotic properties of the proposed approach under mild conditions. The finite-sample performance of the approach is examined b...
