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作者：Portnoy, S
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Using quantile regression to analyze survival times offers an valuable complement to traditional Cox proportional hazards modelling. Unfortunately, this approach has been hampered by the lack of a conditional quantile estimator for censored data that is directly analogous to the Kaplan-Meier estimator and applies under standard assumptions for censored regression models. Here a recursively reweighted estimator of the regression quantile process is developed as a direct generalization of the Ka...
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作者：Cook, RD; Li, LX
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of California System; University of California Davis
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作者：Ling, SQ
作者单位：Hong Kong University of Science & Technology
摘要：This article considers the fractionally autoregressive integrated moving average [ARFIMA(p, d, q)] models with GARCH errors. The process generated by this model is short memory, long memory, stationary, and nonstationary, respectively, when d is an element of (- 1/2, 0), d is an element of (0, 1/2), d is an element of (- 1/2, 1/2), and d is an element of (1/2, infinity). Using a unified approach, the local asymptotic normality of the model is established for d is an element of U-j=0(infinity)(...
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作者：He, XM; Zhu, LX
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Hong Kong; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
摘要：We propose an omnibus lack-of-fit test for linear or nonlinear quantile regression based on a cusum process of the gradient vector. The test does not involve nonparametric smoothing but is consistent for all nonparametric alternatives without any moment conditions on the regression error. In addition, the test is suitable for detecting the local alternatives of any order arbitrarily close to n(-1/2) from the null hypothesis. The limiting distribution of the proposed test statistic is non-Gauss...
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作者：Banerjee, S; Gelfand, AE; Sirmans, CF
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Duke University; University of Connecticut
摘要：Spatial process models are now widely used for inference in many areas of application. In such contexts interest is often in the rate of change of a spatial surface at a given location in a given direction. Examples include temperature or rainfall gradients in meteorology, pollution gradients for environmental data, and surface roughness assessment for digital elevation models. Because the spatial surface is viewed as a random realization, all such rates of change are random as well. We formal...
