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作者：Wang, Lan; Qu, Annie
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Oregon State University
摘要：A robust approach for testing the parametric form of a regression function versus an omnibus alternative is introduced. This generalizes existing robust methods for testing subhypotheses in a regression model. The new test is motivated by developments in modern smoothing-based testing procedures and can be viewed as a robustification of a smoothing-based conditional moment test. It is asymptotically normal under both the null hypothesis and local alternatives. The robustified test retains the ...
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作者：Fried, Roland; Einbeck, Jochen; Gather, Ursula
作者单位：Dortmund University of Technology; Durham University
摘要：We propose weighted repeated median filters and smoothers for robust nonparametric regression in general and for robust online signal extraction from time series in particular. The new methods allow us to remove outlying sequences and to preserve discontinuities (shifts) in the underlying regression function (the signal) in the presence of local linear trends. Suitable weighting of the observations according to their distances in the design space reduces the bias arising from nonlinearities an...
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作者：Lazar, Nicole A.
作者单位：University System of Georgia; University of Georgia
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作者：Gneiting, Tilmann; Raftery, Adrian E.
作者单位：University of Washington; University of Washington Seattle
摘要：Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper. if the forecaster maximizes the expected score for an observation drawn from the distribution F if he or she issues the probabilistic forecast F, rather than G 4 F. It is strictly proper if the maximum is unique. In prediction problems, proper scoring rules encourage the forecaster to make careful...
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作者：Mu, Yunming; He, Xuming
作者单位：Texas A&M University System; Texas A&M University College Station; University of Illinois System; University of Illinois Urbana-Champaign
摘要：In this article we consider the linear quantile regression model with a power transformation on the dependent variable. Like the classical Box-Cox transformation approach, it extends the applicability of linear models without resorting to nonparametric smoothing, but transformations on the quantile models are more natural due to the equivariance property of the quantiles under monotone transformations. We propose an estimation procedure and establish its consistency and asymptotic normality un...
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作者：Cheung, Ying Kuen
作者单位：Columbia University
摘要：This article considers the problem of finding the maximum tolerated dose (MTD) of a drug in human trials. The MTD is defined as the maximum test dose with toxicity probability less than or equal to a target toxicity rate. We adopt the multiple test framework, with step-down tests used in an escalation stage and step-up tests used in a deescalation stage, to allow sequential dose assignments for ethical purposes. By formulating the estimation problem as a testing problem, the proposed procedure...
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作者：Ibrahim, Joseph G.; Zhu, Hongtu
作者单位：University of North Carolina; University of North Carolina Chapel Hill
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作者：Noh, Maengseok; Lee, Youngjo
作者单位：Pukyong National University; Seoul National University (SNU)
摘要：Generalized linear models (GLMs) are widely used for data analysis; however, their maximum likelihood estimators can be sensitive to outliers. We propose new statistical models that allow robust inferences from the GLM class of models, including Poisson and binomial GLMs, and their extension to generalized linear mixed models. The likelihood score equations from the new models give estimators with bounded influence, so that the resulting estimators are robust against outliers while maintaining...
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作者：Cai, Jianwen; Fan, Jianqing; Jiang, Jiancheng; Zhou, Haibo
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Princeton University; University of North Carolina; University of North Carolina Charlotte
摘要：This article studies estimation of partially linear hazard regression models for multivariate survival data. A profile pseudo-partial likelihood estimation method is proposed under the marginal hazard model framework. The estimation on the parameters for the linear part is accomplished by maximization of a pseudo-partial likelihood profiled over the nonparametric part. This enables us to obtain root n-consistent estimators of the parametric component. Asymptotic normality is obtained for the e...
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作者：Gomes, M. Ivette; Pestana, Dinis
作者单位：Universidade de Lisboa
摘要：The main objective of statistics of extremes lies in the estimation of quantities related to extreme events. In many areas of application, such as statistical quality control, insurance, and finance, a typical requirement is to estimate a high quantile, that is, the value at risk at a level p (VaR(p)), high enough so that the chance of an exceedance of that value is equal to p, small. In this article we deal with the semiparametric estimation of VaRp for heavy tails. The classical semiparametr...
