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作者：Chattopadhyay, Ambarish; Zubizarreta, Jose R.
作者单位：Stanford University; Harvard University; Harvard University; Harvard University
摘要：A basic principle in the design of observational studies is to approximate the randomized experiment that would have been conducted under ideal circumstances. At present, linear regression models are commonly used to analyse observational data and estimate causal effects. How do linear regression adjustments in observational studies emulate key features of randomized experiments, such as covariate balance, self-weighted sampling and study representativeness? In this paper, we provide answers t...
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作者：Masak, T.; Sarkar, S.; Panaretos, V. M.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：The nonparametric estimation of covariance lies at the heart of functional data analysis, whether for curve or surface-valued data. The case of a two-dimensional domain poses both statistical and computational challenges, which are typically alleviated by assuming separability. However, separability is often questionable, sometimes even demonstrably inadequate. We propose a framework for the analysis of covariance operators of random surfaces that generalizes separability while retaining its m...
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作者：Zhou, Zheng; Zhou, Yongdao
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作者：Song, Peter X-K; Zhou, Ling
作者单位：University of Michigan System; University of Michigan; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China
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作者：Dimitriadis, Timo; Dumbgen, Lutz; Henzi, Alexander; Puke, Marius; Ziegel, Johanna
作者单位：Ruprecht Karls University Heidelberg; University of Bern; Swiss Federal Institutes of Technology Domain; ETH Zurich; University Hohenheim
摘要：Probability predictions from binary regressions or machine learning methods ought to be calibrated: if an event is predicted to occur with probability x, it should materialize with approximately that frequency, which means that the so-called calibration curvep(middot) should equal the identity, i.e., p(x) = x for all x in the unit interval. We propose honest calibration assessment based on novel confidence bands for the calibration curve, which are valid subject to only the natural assumption ...
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作者：Xu, Yangjianchen; Zeng, Donglin; Lin, D. Y.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseu...
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作者：Guan, Leying
作者单位：Yale University
摘要：We propose a new inference framework called localized conformal prediction. It generalizes the framework of conformal prediction by offering a single-test-sample adaptive construction that emphasizes a local region around this test sample, and can be combined with different conformal scores. The proposed framework enjoys an assumption-free finite sample marginal coverage guarantee, and it also offers additional local coverage guarantees under suitable assumptions. We demonstrate how to change ...
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作者：Kuchibhotla, Arun Kumar; Balakrishnan, Sivaraman; Wasserman, Larry
作者单位：Carnegie Mellon University
摘要：We introduce a new notion of regularity of an estimator called median regularity. We prove that uniformly valid honest inference for a functional is possible if and only if there exists a median regular estimator of that functional. To the best of our knowledge, such a notion of regularity that is necessary for uniformly valid inference is unavailable in the literature.
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作者：Czado, C.; Van Keilegom, I
作者单位：Technical University of Munich; KU Leuven
摘要：Consider a survival time T that is subject to random right censoring, and suppose that T is stochastically dependent on the censoring time C. We are interested in the marginal distribution of T. This situation is often encountered in practice. Consider, for example, the case where T is a patient's time to death from a certain disease. Then the censoring time C could be the time until the patient leaves the study or the time until death from another cause. If the reason for leaving the study is...
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作者：Rasines, D. Garcia; Young, G. A.
作者单位：Imperial College London
摘要：We consider the problem of providing valid inference for a selected parameter in a sparse regression setting. It is well known that classical regression tools can be unreliable in this context because of the bias generated in the selection step. Many approaches have been proposed in recent years to ensure inferential validity. In this article we consider a simple alternative to data splitting based on randomizing the response vector, which allows for higher selection and inferential power than...
