Regression Quantiles and Related Processes Under Long Range Dependent Errors
This paper obtains asymptotic representations of the regression quantiles and the regression rank-scores processes in linear regression setting when the errors are a function of Gaussian random variables that ale stationary and long range dependent. These representations are then used to obtain the limiting behavior of L- and linear regression rank-scores statistics based on the above processes. The paper also obtains the asymptotic uniform linearity of the linear regression rank-scores processes and statistics based on residuals under the long range dependent setup. It thus generalizes some of the results of Jurecková [In Proceedings of the Meeting on Nonparametric Statistics and Related topics (A. K. Md. E. Saleh, Ed.) pp. 217-228. Elsevier, Amsterdam/New York] and Gutenbrunner and Jurecková [Ann. Statist. 20 305-329] for the case of independent errors to one of the highly useful dependent errors setup.
Year of publication: |
1994
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Authors: | Koul, H. L. ; Mukherjee, K. |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 51.1994, 2, p. 318-337
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Publisher: |
Elsevier |
Saved in:
Online Resource
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