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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...
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作者：Barreiro-Ures, D.; Cao, R.; Francisco-Fernandez, M.; Hart, J. D.
作者单位：Universidade da Coruna; Texas A&M University System; Texas A&M University College Station
摘要：Hall & Robinson (2009) proposed and analysed the use of bagged cross-validation to choose the bandwidth of a kernel density estimator. They established that bagging greatly reduces the noise inherent in ordinary cross-validation, and hence leads to a more efficient bandwidth selector. The asymptotic theory of Hall & Robinson (2009) assumes that N, the number of bagged subsamples, is 8. We expand upon their theoretical results by allowing N to be finite, as it is in practice. Our results indica...
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作者：Solus, L.; Wang, Y.; Uhler, C.
作者单位：Royal Institute of Technology; Tsinghua University; Massachusetts Institute of Technology (MIT)
摘要：Directed acyclic graphical models are widely used to represent complex causal systems. Since the basic task of learning such a model from data is NP-hard, a standard approach is greedy search over the space of directed acyclic graphs or Markov equivalence classes of directed acyclic graphs. As the space of directed acyclic graphs on p nodes and the associated space of Markov equivalence classes are both much larger than the space of permutations, it is desirable to consider permutation-based g...
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作者：Garside, K.; Gjoka, A.; Henderson, R.; Johnson, H.; Makarenko, I
作者单位：Newcastle University - UK
摘要：Persistent homology is used to track the appearance and disappearance of features as we move through a nested sequence of topological spaces. Equating the nested sequence to a filtration and the appearance and disappearance of features to events, we show that simple event history methods can be used for the analysis of topological data. We propose a version of the well-known Nelson-Aalen cumulative hazard estimator for the comparison of topological features of random fields and for testing par...
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作者：Qian, Tianchen; Yoo, Hyesun; Klasnja, Predrag; Almirall, Daniel; Murphy, Susan A.
作者单位：University of California System; University of California Irvine; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; Harvard University
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作者：Jentsch, Carsten; Lee, Eun Ryung; Mammen, Enno
作者单位：Dortmund University of Technology; Sungkyunkwan University (SKKU); Ruprecht Karls University Heidelberg
摘要：We discuss Poisson reduced-rank models for low-dimensional summaries of high-dimensional Poisson vectors that allow inference on the location of individuals in a low-dimensional space. We show that under weak dependence conditions, which allow for certain correlations between the Poisson random variables, the locations can be consistently estimated using Poisson maximum likelihood estimation. Moreover, we develop consistent rules for determining the dimension of the location from the discrete ...
