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Minimum distance estimation in linear models with long range dependent errors.

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Minimum distance estimation in linear models with long range dependent errors. / Mukherjee, Kanchan.
In: Statistics and Probability Letters, Vol. 21, No. 5, 07.12.1994, p. 347-355.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Mukherjee K. Minimum distance estimation in linear models with long range dependent errors. Statistics and Probability Letters. 1994 Dec 7;21(5):347-355. doi: 10.1016/0167-7152(94)00029-8

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Mukherjee, Kanchan. / Minimum distance estimation in linear models with long range dependent errors. In: Statistics and Probability Letters. 1994 ; Vol. 21, No. 5. pp. 347-355.

Bibtex

@article{1a7fbe7ffbb3481e884f49ccfb130413,
title = "Minimum distance estimation in linear models with long range dependent errors.",
abstract = "This paper discusses the asymptotic representations of a class of L2-distance estimators based on weighted empirical processes in a multiple linear regression model when the errors are a function of stationary Gaussian random variables that are long-range dependent. Unlike the independent errors case, the limiting distributions of the suitably normalized estimators are not always normal. The limiting distributions depend heavily on the Hermite rank of a certain class of random variables. Some {\textquoteleft}goodness of fit{\textquoteright} tests for specified error distribution are also considered. ",
keywords = "Asymptotic uniform quadraticity, Long-range dependence , Hermite ranks and polynominals",
author = "Kanchan Mukherjee",
year = "1994",
month = dec,
day = "7",
doi = "10.1016/0167-7152(94)00029-8",
language = "English",
volume = "21",
pages = "347--355",
journal = "Statistics and Probability Letters",
issn = "0167-7152",
publisher = "Elsevier",
number = "5",

}

RIS

TY - JOUR

T1 - Minimum distance estimation in linear models with long range dependent errors.

AU - Mukherjee, Kanchan

PY - 1994/12/7

Y1 - 1994/12/7

N2 - This paper discusses the asymptotic representations of a class of L2-distance estimators based on weighted empirical processes in a multiple linear regression model when the errors are a function of stationary Gaussian random variables that are long-range dependent. Unlike the independent errors case, the limiting distributions of the suitably normalized estimators are not always normal. The limiting distributions depend heavily on the Hermite rank of a certain class of random variables. Some ‘goodness of fit’ tests for specified error distribution are also considered.

AB - This paper discusses the asymptotic representations of a class of L2-distance estimators based on weighted empirical processes in a multiple linear regression model when the errors are a function of stationary Gaussian random variables that are long-range dependent. Unlike the independent errors case, the limiting distributions of the suitably normalized estimators are not always normal. The limiting distributions depend heavily on the Hermite rank of a certain class of random variables. Some ‘goodness of fit’ tests for specified error distribution are also considered.

KW - Asymptotic uniform quadraticity

KW - Long-range dependence

KW - Hermite ranks and polynominals

U2 - 10.1016/0167-7152(94)00029-8

DO - 10.1016/0167-7152(94)00029-8

M3 - Journal article

VL - 21

SP - 347

EP - 355

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

SN - 0167-7152

IS - 5

ER -