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Testing significance of variables in regression analysis when there is non-normality or heteroskedasticity.: The wild bootstrap and the generalised lambda distribution

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Testing significance of variables in regression analysis when there is non-normality or heteroskedasticity. The wild bootstrap and the generalised lambda distribution. / Pavlidis, E.; Paya, I.; Peel, D. A.
Advances In Doctoral Research In Management (Volume 2). World Scientific Publishing Co., 2008. p. 151-174.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Pavlidis, E, Paya, I & Peel, DA 2008, Testing significance of variables in regression analysis when there is non-normality or heteroskedasticity. The wild bootstrap and the generalised lambda distribution. in Advances In Doctoral Research In Management (Volume 2). World Scientific Publishing Co., pp. 151-174. https://doi.org/10.1142/9789812778666_0008

APA

Pavlidis, E., Paya, I., & Peel, D. A. (2008). Testing significance of variables in regression analysis when there is non-normality or heteroskedasticity. The wild bootstrap and the generalised lambda distribution. In Advances In Doctoral Research In Management (Volume 2) (pp. 151-174). World Scientific Publishing Co.. https://doi.org/10.1142/9789812778666_0008

Vancouver

Pavlidis E, Paya I, Peel DA. Testing significance of variables in regression analysis when there is non-normality or heteroskedasticity. The wild bootstrap and the generalised lambda distribution. In Advances In Doctoral Research In Management (Volume 2). World Scientific Publishing Co. 2008. p. 151-174 doi: 10.1142/9789812778666_0008

Author

Pavlidis, E. ; Paya, I. ; Peel, D. A. / Testing significance of variables in regression analysis when there is non-normality or heteroskedasticity. The wild bootstrap and the generalised lambda distribution. Advances In Doctoral Research In Management (Volume 2). World Scientific Publishing Co., 2008. pp. 151-174

Bibtex

@inbook{889830814ce44c5d93dce174b28ad671,
title = "Testing significance of variables in regression analysis when there is non-normality or heteroskedasticity.: The wild bootstrap and the generalised lambda distribution",
abstract = "Statistical inference on the parameters of regression models requires special precautions when the error term is heteroskedastic and/or non-normal. In this case, although conventional test statistics do not follow t and F distributions, simulation methods can be used to draw inferences. We discuss two methods: the wild bootstrap and the generalised lambda distribution. By employing both artificial and real-world data from the National Footbal League, we show that these methods may prove particularly useful in hypothesis testing.",
keywords = "Generalised lambda distribution, Heteroskedastic, Monte carlo simulations, Non-normality, Wild bootstrap",
author = "E. Pavlidis and I. Paya and Peel, {D. A.}",
note = "Publisher Copyright: {\textcopyright} 2008 by World Scientific Publishing Co. Pte. Ltd.",
year = "2008",
month = jan,
day = "1",
doi = "10.1142/9789812778666_0008",
language = "English",
pages = "151--174",
booktitle = "Advances In Doctoral Research In Management (Volume 2)",
publisher = "World Scientific Publishing Co.",
address = "United States",

}

RIS

TY - CHAP

T1 - Testing significance of variables in regression analysis when there is non-normality or heteroskedasticity.

T2 - The wild bootstrap and the generalised lambda distribution

AU - Pavlidis, E.

AU - Paya, I.

AU - Peel, D. A.

N1 - Publisher Copyright: © 2008 by World Scientific Publishing Co. Pte. Ltd.

PY - 2008/1/1

Y1 - 2008/1/1

N2 - Statistical inference on the parameters of regression models requires special precautions when the error term is heteroskedastic and/or non-normal. In this case, although conventional test statistics do not follow t and F distributions, simulation methods can be used to draw inferences. We discuss two methods: the wild bootstrap and the generalised lambda distribution. By employing both artificial and real-world data from the National Footbal League, we show that these methods may prove particularly useful in hypothesis testing.

AB - Statistical inference on the parameters of regression models requires special precautions when the error term is heteroskedastic and/or non-normal. In this case, although conventional test statistics do not follow t and F distributions, simulation methods can be used to draw inferences. We discuss two methods: the wild bootstrap and the generalised lambda distribution. By employing both artificial and real-world data from the National Footbal League, we show that these methods may prove particularly useful in hypothesis testing.

KW - Generalised lambda distribution

KW - Heteroskedastic

KW - Monte carlo simulations

KW - Non-normality

KW - Wild bootstrap

U2 - 10.1142/9789812778666_0008

DO - 10.1142/9789812778666_0008

M3 - Chapter

AN - SCOPUS:85115948325

SP - 151

EP - 174

BT - Advances In Doctoral Research In Management (Volume 2)

PB - World Scientific Publishing Co.

ER -