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    Rights statement: http://journals.cambridge.org/action/displayJournal?jid=ECT The final, definitive version of this article has been published in the Journal, Econometric Theory, 16 (3), pp 301-323 2000, © 2000 Cambridge University

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Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles

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Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles. / Mukherjee, Kanchan.
In: Econometric Theory, Vol. 16, No. 3, 06.2000, p. 301-323.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Mukherjee, K 2000, 'Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles', Econometric Theory, vol. 16, no. 3, pp. 301-323. <http://journals.cambridge.org/action/displayJournal?jid=ECT>

APA

Mukherjee, K. (2000). Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles. Econometric Theory, 16(3), 301-323. http://journals.cambridge.org/action/displayJournal?jid=ECT

Vancouver

Mukherjee K. Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles. Econometric Theory. 2000 Jun;16(3):301-323.

Author

Mukherjee, Kanchan. / Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles. In: Econometric Theory. 2000 ; Vol. 16, No. 3. pp. 301-323.

Bibtex

@article{b876db27d4814eafaf7d9b041a9020fe,
title = "Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles",
abstract = "This paper discusses some asymptotic uniform linearity results of randomly weighted empirical processes based on long range dependent random variables+ These results are subsequently used to linearize nonlinear regression quantiles in a nonlinear regression model with long range dependent errors, where the design variables can be either random or nonrandom+ These, in turn, yield the limiting behavior of the nonlinear regression quantiles+ As a corollary, we obtain the limiting behavior of the least absolute deviation estimator and the trimmed mean estimator of the parameters of the nonlinear regression model+ Some of the limiting properties are in striking contrast with the corresponding properties of a nonlinear regression model under independent and identically distributed error random variables+ The paper also discusses an extension of rank score statistic in a nonlinear regression model+",
keywords = "Nonlinear quantiles, long range dependence",
author = "Kanchan Mukherjee",
note = "http://journals.cambridge.org/action/displayJournal?jid=ECT The final, definitive version of this article has been published in the Journal, Econometric Theory, 16 (3), pp 301-323 2000, {\textcopyright} 2000 Cambridge University ",
year = "2000",
month = jun,
language = "English",
volume = "16",
pages = "301--323",
journal = "Econometric Theory",
issn = "1469-4360",
publisher = "Cambridge University Press",
number = "3",

}

RIS

TY - JOUR

T1 - Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles

AU - Mukherjee, Kanchan

N1 - http://journals.cambridge.org/action/displayJournal?jid=ECT The final, definitive version of this article has been published in the Journal, Econometric Theory, 16 (3), pp 301-323 2000, © 2000 Cambridge University

PY - 2000/6

Y1 - 2000/6

N2 - This paper discusses some asymptotic uniform linearity results of randomly weighted empirical processes based on long range dependent random variables+ These results are subsequently used to linearize nonlinear regression quantiles in a nonlinear regression model with long range dependent errors, where the design variables can be either random or nonrandom+ These, in turn, yield the limiting behavior of the nonlinear regression quantiles+ As a corollary, we obtain the limiting behavior of the least absolute deviation estimator and the trimmed mean estimator of the parameters of the nonlinear regression model+ Some of the limiting properties are in striking contrast with the corresponding properties of a nonlinear regression model under independent and identically distributed error random variables+ The paper also discusses an extension of rank score statistic in a nonlinear regression model+

AB - This paper discusses some asymptotic uniform linearity results of randomly weighted empirical processes based on long range dependent random variables+ These results are subsequently used to linearize nonlinear regression quantiles in a nonlinear regression model with long range dependent errors, where the design variables can be either random or nonrandom+ These, in turn, yield the limiting behavior of the nonlinear regression quantiles+ As a corollary, we obtain the limiting behavior of the least absolute deviation estimator and the trimmed mean estimator of the parameters of the nonlinear regression model+ Some of the limiting properties are in striking contrast with the corresponding properties of a nonlinear regression model under independent and identically distributed error random variables+ The paper also discusses an extension of rank score statistic in a nonlinear regression model+

KW - Nonlinear quantiles

KW - long range dependence

M3 - Journal article

VL - 16

SP - 301

EP - 323

JO - Econometric Theory

JF - Econometric Theory

SN - 1469-4360

IS - 3

ER -