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作者：Demirkaya, Emre; Feng, Yang; Basu, Pallavi; Lv, Jinchi
作者单位：University of Tennessee System; University of Tennessee Knoxville; New York University; Indian School of Business (ISB); University of Southern California
摘要：Model selection is crucial both to high-dimensional learning and to inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate interpretable models. Most existing work implicitly assumes that the models are correctly specified or have fixed dimensionality, yet both model misspecification and high dimensionality are prevalent in practice. In this paper, we exploit the framework of model selection principles under the misspecified gen...
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作者：Ye, Ting; Yi, Yanyao; Shao, Jun
作者单位：University of Pennsylvania; Eli Lilly; East China Normal University
摘要：Covariate-adaptive randomization schemes such as minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theory for inference after covariate-adaptive randomization is mostly limited to situations where a correct model between the response and covariates can be specified or the randomization method has well-understood properties. Based on stratification with covariate levels utilized in randomizat...
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作者：Escobar-Bach, Mikael; Maller, Ross; Van Keilegom, Ingrid; Zhao, Muzhi
作者单位：Universite d'Angers; Australian National University; KU Leuven
摘要：Estimators of the cured proportion from survival data which may include observations on cured subjects can only be expected to perform well when the follow-up period is sufficient. When follow-up is not sufficient, and the survival distribution of those susceptible to the event belongs to the Frechet maximum domain of attraction, a nonparametric estimator for the cure proportion proposed by incorporates an adjustment that reduces the bias in the usual estimator. Besides the Frechet, an importa...
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作者：Park, Juhyun; Ahn, Jeongyoun; Jeon, Yongho
作者单位：Universite Paris Saclay; Universite Paris Saclay; University System of Georgia; University of Georgia; Yonsei University
摘要：Functional linear discriminant analysis provides a simple yet efficient method for classification, with the possibility of achieving perfect classification. Several methods have been proposed in the literature that mostly address the dimensionality of the problem. On the other hand, there is growing interest in interpretability of the analysis, which favours a simple and sparse solution. In this paper we propose a new approach that incorporates a type of sparsity that identifies nonzero subdom...
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作者：Tan, Falong; Zhu, Lixing
作者单位：Hunan University; Beijing Normal University; Beijing Normal University Zhuhai
摘要：The classical integrated conditional moment test is a promising method for model checking and its basic idea has been applied to develop several variants. However, in diverging-dimension scenarios, the integrated conditional moment test may break down and has completely different limiting properties from the fixed-dimension case. Furthermore, the related wild bootstrap approximation can also be invalid. To extend this classical test to diverging dimension settings, we propose a projected adapt...
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作者：Chen, Liujun; Li, Deyuan; Zhou, Chen
作者单位：Fudan University; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam
摘要：In this paper we investigate a divide-and-conquer algorithm for estimating the extreme value index when data are stored in multiple machines. The oracle property of such an algorithm based on extreme value methods is not guaranteed by the general theory of distributed inference. We propose a distributed Hill estimator and establish its asymptotic theories. We consider various cases where the number of observations involved in each machine can be either homogeneous or heterogeneous, and either ...
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作者：Farewell, D. M.; Daniel, R. M.; Seaman, S. R.
作者单位：Cardiff University; MRC Biostatistics Unit; University of Cambridge
摘要：We offer a natural and extensible measure-theoretic treatment of missingness at random. Within the standard missing-data framework, we give a novel characterization of the observed data as a stopping-set sigma algebra. We demonstrate that the usual missingness-at-random conditions are equivalent to requiring particular stochastic processes to be adapted to a set-indexed filtration. These measurability conditions ensure the usual factorization of likelihood ratios. We illustrate how the theory ...
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作者：Sun, Yifei; Chiou, Sy Han; Marr, Kieren A.; Huang, Chiung-Yu
作者单位：Columbia University; University of Texas System; University of Texas Dallas; Johns Hopkins University; University of California System; University of California San Francisco
摘要：Single-index models have gained increased popularity in time-to-event analysis owing to their model flexibility and advantage in dimension reduction. We propose a semiparametric framework for the rate function of a recurrent event counting process by modelling its size and shape components with single-index models. With additional monotone constraints on the two link functions for the size and shape components, the proposed model possesses the desired directional interpretability of covariate ...
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作者：Kong, Dehan; Yang, Shu; Wang, Linbo
作者单位：University of Toronto; North Carolina State University
摘要：Unobserved confounding presents a major threat to causal inference in observational studies. Recently, several authors have suggested that this problem could be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effect is not identifiable without parametric assumptions on the outcome model. In this note, we show that the causal effect is indeed i...
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作者：Smucler, E.; Sapienza, F.; Rotnitzky, A.
作者单位：University of California System; University of California Berkeley; Universidad Torcuato Di Tella
摘要：We study the selection of adjustment sets for estimating the interventional mean under a point exposure dynamic treatment regime, that is, a treatment rule that depends on the subject's covariates. We assume a nonparametric causal graphical model with, possibly, hidden variables and at least one adjustment set comprised of observable variables. We provide the definition of a valid adjustment set for a point exposure dynamic treatment regime, which generalizes the existing definition for a stat...
