Regression Quantiles and Related Processes Under Long Range Dependent Errors
✍ Scribed by H.L. Koul; K. Mukherjee
- Publisher
- Elsevier Science
- Year
- 1994
- Tongue
- English
- Weight
- 681 KB
- Volume
- 51
- Category
- Article
- ISSN
- 0047-259X
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✦ Synopsis
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 are 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 rankscores processes and statistics based on residuals under the long range dependent setup. It thus generalizes some of the results of Jurečkova [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 Jurečková [Ann. Statist. 20 305-329] for the case of independent errors to one of the highly useful dependent errors setup. 1994 Academic Press. Inc.