Shrinkage-based semiparametric density estimation

S. Ejaz Ahmed; Mohamed Amezziane

doi:10.1016/j.stamet.2016.10.004

Shrinkage-based semiparametric density estimation

S. Ejaz Ahmed, Mohamed Amezziane

Statistics, Actuarial & Data Sciences

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

1 Scopus citations

Abstract

Shrinkage estimation is used to develop a semiparametric density estimator as a linear combination of a fully known parametric density function and a nonparametric density estimator. We determine the asymptotic properties of the shrinkage coefficient and of the semiparametric estimator's integrated squared error. Moreover, we show that the proposed estimation methodology delivers density estimators that are more accurate than nonparametric estimators and that do not require the use of optimal smoothing parameters.

Original language	English
Journal	Statistical Methodology
DOIs	https://doi.org/10.1016/j.stamet.2016.10.004
State	Accepted/In press - Dec 10 2015

Keywords

Asymptotic statistics
Curse of dimensionality
Density estimation
Semiparametric model
Shrinkage estimation

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10.1016/j.stamet.2016.10.004

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title = "Shrinkage-based semiparametric density estimation",

abstract = "Shrinkage estimation is used to develop a semiparametric density estimator as a linear combination of a fully known parametric density function and a nonparametric density estimator. We determine the asymptotic properties of the shrinkage coefficient and of the semiparametric estimator's integrated squared error. Moreover, we show that the proposed estimation methodology delivers density estimators that are more accurate than nonparametric estimators and that do not require the use of optimal smoothing parameters.",

keywords = "Asymptotic statistics, Curse of dimensionality, Density estimation, Semiparametric model, Shrinkage estimation",

author = "{Ejaz Ahmed}, S. and Mohamed Amezziane",

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year = "2015",

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AU - Amezziane, Mohamed

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N2 - Shrinkage estimation is used to develop a semiparametric density estimator as a linear combination of a fully known parametric density function and a nonparametric density estimator. We determine the asymptotic properties of the shrinkage coefficient and of the semiparametric estimator's integrated squared error. Moreover, we show that the proposed estimation methodology delivers density estimators that are more accurate than nonparametric estimators and that do not require the use of optimal smoothing parameters.

AB - Shrinkage estimation is used to develop a semiparametric density estimator as a linear combination of a fully known parametric density function and a nonparametric density estimator. We determine the asymptotic properties of the shrinkage coefficient and of the semiparametric estimator's integrated squared error. Moreover, we show that the proposed estimation methodology delivers density estimators that are more accurate than nonparametric estimators and that do not require the use of optimal smoothing parameters.

KW - Asymptotic statistics

KW - Curse of dimensionality

KW - Density estimation

KW - Semiparametric model

KW - Shrinkage estimation

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AN - SCOPUS:85048737191

SN - 1572-3127

JO - Statistical Methodology

JF - Statistical Methodology

ER -

Shrinkage-based semiparametric density estimation

Abstract

Keywords

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