Nonparametric covariance model

Jianxin Yin; Zhi Geng; Runze Li; Hansheng Wang

Nonparametric covariance model

Jianxin Yin, Zhi Geng, Runze Li, Hansheng Wang

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

Abstract

There has been considerable attention paid to estimation of conditional variance functions in the literature. We propose a nonparametric model for the conditional covariance matrix. A kernel estimator is developed, its asymptotic bias and variance are derived, and its asymptotic normality is established. A data example is used to illustrate the proposed procedure.

Original language	English (US)
Pages (from-to)	469-479
Number of pages	11
Journal	Statistica Sinica
Volume	20
Issue number	1
State	Published - Jan 2010

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

Cite this

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title = "Nonparametric covariance model",

abstract = "There has been considerable attention paid to estimation of conditional variance functions in the literature. We propose a nonparametric model for the conditional covariance matrix. A kernel estimator is developed, its asymptotic bias and variance are derived, and its asymptotic normality is established. A data example is used to illustrate the proposed procedure.",

author = "Jianxin Yin and Zhi Geng and Runze Li and Hansheng Wang",

year = "2010",

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AU - Yin, Jianxin

AU - Geng, Zhi

AU - Li, Runze

AU - Wang, Hansheng

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N2 - There has been considerable attention paid to estimation of conditional variance functions in the literature. We propose a nonparametric model for the conditional covariance matrix. A kernel estimator is developed, its asymptotic bias and variance are derived, and its asymptotic normality is established. A data example is used to illustrate the proposed procedure.

AB - There has been considerable attention paid to estimation of conditional variance functions in the literature. We propose a nonparametric model for the conditional covariance matrix. A kernel estimator is developed, its asymptotic bias and variance are derived, and its asymptotic normality is established. A data example is used to illustrate the proposed procedure.

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EP - 479

JO - Statistica Sinica

JF - Statistica Sinica

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Nonparametric covariance model

Abstract

All Science Journal Classification (ASJC) codes

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