VARIABLE SELECTION BY PSEUDO WAVELETS IN HETEROSCEDASTIC REGRESSION MODELS INVOLVING TIME SERIES* * Zhou's research was partially supported by the foundations of National Natural Science (10471140) and (10571169) of China.

Qinghe Wang; Yong Zhou

doi:10.1016/S0252-9602(06)60071-4

VARIABLE SELECTION BY PSEUDO WAVELETS IN HETEROSCEDASTIC REGRESSION MODELS INVOLVING TIME SERIES^* * Zhou's research was partially supported by the foundations of National Natural Science (10471140) and (10571169) of China.

Qinghe Wang^*
, Yong Zhou

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

A simple but efficient method has been proposed to select variables in heteroscedastic regression models. It is shown that the pseudo empirical wavelet coefficients corresponding to the significant explanatory variables in the regression models are clearly larger than those nonsignificant ones, on the basis of which a procedure is developed to select variables in regression models. The coefficients of the models are also estimated. All estimators are proved to be consistent.

Original language	English
Pages (from-to)	469-476
Number of pages	8
Journal	Acta Mathematica Scientia
Volume	26
Issue number	3
DOIs	https://doi.org/10.1016/S0252-9602(06)60071-4
State	Published - 2006
Externally published	Yes

Keywords

Heteroscedastic regression models
variable selection
wavelets

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10.1016/S0252-9602(06)60071-4

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title = "VARIABLE SELECTION BY PSEUDO WAVELETS IN HETEROSCEDASTIC REGRESSION MODELS INVOLVING TIME SERIES* * Zhou's research was partially supported by the foundations of National Natural Science (10471140) and (10571169) of China.",

abstract = "A simple but efficient method has been proposed to select variables in heteroscedastic regression models. It is shown that the pseudo empirical wavelet coefficients corresponding to the significant explanatory variables in the regression models are clearly larger than those nonsignificant ones, on the basis of which a procedure is developed to select variables in regression models. The coefficients of the models are also estimated. All estimators are proved to be consistent.",

keywords = "Heteroscedastic regression models, variable selection, wavelets",

author = "Qinghe Wang and Yong Zhou",

year = "2006",

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language = "英语",

volume = "26",

pages = "469--476",

journal = "Acta Mathematica Scientia",

issn = "0252-9602",

publisher = "Springer",

number = "3",

}

VARIABLE SELECTION BY PSEUDO WAVELETS IN HETEROSCEDASTIC REGRESSION MODELS INVOLVING TIME SERIES^* * Zhou's research was partially supported by the foundations of National Natural Science (10471140) and (10571169) of China. / Wang, Qinghe; Zhou, Yong.
In: Acta Mathematica Scientia, Vol. 26, No. 3, 2006, p. 469-476.

Research output: Contribution to journal › Article › peer-review

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AU - Zhou, Yong

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KW - variable selection

KW - wavelets

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