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作者：Chang, Jinyuan; Yao, Qiwei; Zhou, Wen
作者单位：Southwestern University of Finance & Economics - China; University of London; London School Economics & Political Science; Colorado State University System; Colorado State University Fort Collins
摘要：We propose a new omnibus test for vector white noise using the maximum absolute auto-correlations and cross-correlations of the component series. Based on an approximation by the L-infinity-norm of a normal random vector, the critical value of the test can be evaluated by bootstrapping from a multivariate normal distribution. In contrast to the conventional white noise test, the new method is proved to be valid for testing departure from white noise that is not independent and identically dist...
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作者：Shin, Seung Jun; Wu, Yichao; Zhang, Hao Helen; Liu, Yufeng
作者单位：Korea University; North Carolina State University; University of Arizona; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are...
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作者：Ogden, H. E.
作者单位：University of Southampton
摘要：Many statistical models have likelihoods which are intractable: it is impossible or too expensive to compute the likelihood exactly. In such settings, a common approach is to replace the likelihood with an approximation, and proceed with inference as if the approximate likelihood were the true likelihood. In this paper, we describe conditions which guarantee that such naive inference with an approximate likelihood has the same first-order asymptotic properties as inference with the true likeli...
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作者：Johnstone, I. M.; Nadler, B.
作者单位：Stanford University; Weizmann Institute of Science
摘要：Roy's largest root is a common test statistic in multivariate analysis, statistical signal processing and allied fields. Despite its ubiquity, provision of accurate and tractable approximations to its distribution under the alternative has been a longstanding open problem. Assuming Gaussian observations and a rank-one alternative, or concentrated noncentrality, we derive simple yet accurate approximations for the most common low-dimensional settings. These include signal detection in noise, mu...
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作者：Chang, Jinyuan; Yao, Qiwei; Zhou, Wen
作者单位：Southwestern University of Finance & Economics - China; University of London; London School Economics & Political Science; Colorado State University System; Colorado State University Fort Collins
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作者：Wang, Linbo; Robins, James M.; Richardson, Thomas S.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health; University of Washington; University of Washington Seattle
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作者：Bertrand, Aurelie; Legrand, Catherine; Carroll, Raymond J.; De Meester, Christophe; Van Keilegom, Ingrid
作者单位：Universite Catholique Louvain; Texas A&M University System; Texas A&M University College Station; Universite Catholique Louvain
摘要：In many situations in survival analysis, it may happen that a fraction of individuals will never experience the event of interest: they are considered to be cured. The promotion time cure model takes this into account. We consider the case where one or more explanatory variables in the model are subject to measurement error, which should be taken into account to avoid biased estimators. A general approach is the simulation-extrapolation algorithm, a method based on simulations which allows one...
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作者：Chen, Y.; Ning, J.; Ning, Y.; Liang, K. -Y.; Bandeen-Roche, K.
作者单位：University of Pennsylvania; University of Texas System; UTMD Anderson Cancer Center; Cornell University; National Yang Ming Chiao Tung University; Johns Hopkins University
摘要：Consider a semiparametric model indexed by a Euclidean parameter of interest and an infinite-dimensional nuisance parameter. In many applications, pseudolikelihood provides a convenient way to infer the parameter of interest, where the nuisance parameter is replaced by a consistent estimator. The purpose of this paper is to establish the asymptotic behaviour of the pseudolikelihood ratio statistic under semiparametric models. In particular, we consider testing the hypothesis that the parameter...
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作者：Zhou, Q.; Zhou, H.; Cai, J.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：The case-cohort design has been widely used as a means of cost reduction in collecting or measuring expensive covariates in large cohort studies. The existing literature on the case-cohort design is mainly focused on right-censored data. In practice, however, the failure time is often subject to interval-censoring: it is known to fall only within some random time interval. In this paper, we consider the case-cohort study design for interval-censored failure time and develop a sieve semiparamet...
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作者：Cai, J. -J.; Chavez-Demoulin, V.; Guillou, A.
作者单位：Delft University of Technology; University of Lausanne; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg
摘要：We propose an estimator of the marginal expected shortfall by considering a log transformation of a variable which has an infinite expectation. We establish the asymptotic normality of our estimator under general assumptions. A simulation study suggests that the estimation procedure is robust with respect to the choice of tuning parameters. Our estimator has lower bias and mean squared error than the empirical estimator when the latter is applicable. We illustrate our method on a tsunami datas...
