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Particle swarm optimization based Liu-type estimator

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Particle swarm optimization based Liu-type estimator. / Inan, Deniz; Egrioglu, Erol; Sarica, Busenur et al.
In: Communications in Statistics - Theory and Methods, Vol. 46, No. 22, 17.11.2017, p. 11358-11369.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Inan, D, Egrioglu, E, Sarica, B, Askin, OE & Tez, M 2017, 'Particle swarm optimization based Liu-type estimator', Communications in Statistics - Theory and Methods, vol. 46, no. 22, pp. 11358-11369. https://doi.org/10.1080/03610926.2016.1267759

APA

Inan, D., Egrioglu, E., Sarica, B., Askin, O. E., & Tez, M. (2017). Particle swarm optimization based Liu-type estimator. Communications in Statistics - Theory and Methods, 46(22), 11358-11369. https://doi.org/10.1080/03610926.2016.1267759

Vancouver

Inan D, Egrioglu E, Sarica B, Askin OE, Tez M. Particle swarm optimization based Liu-type estimator. Communications in Statistics - Theory and Methods. 2017 Nov 17;46(22):11358-11369. doi: 10.1080/03610926.2016.1267759

Author

Inan, Deniz ; Egrioglu, Erol ; Sarica, Busenur et al. / Particle swarm optimization based Liu-type estimator. In: Communications in Statistics - Theory and Methods. 2017 ; Vol. 46, No. 22. pp. 11358-11369.

Bibtex

@article{88599255eca049008fc8f6f0d99793ea,
title = "Particle swarm optimization based Liu-type estimator",
abstract = "In this study, a new method for the estimation of the shrinkage and biasing parameters of Liu-type estimator is proposed. Because k is kept constant and d is optimized in Liu{\textquoteright}s method, a (k, d) pair is not guaranteed to be the optimal point in terms of the mean square error of the parameters. The optimum (k, d) pair that minimizes the mean square error, which is a function of the parameters k and d, should be estimated through a simultaneous optimization process rather than through a two-stage process. In this study, by utilizing a different objective function, the parameters k and d are optimized simultaneously with the particle swarm optimization technique.",
keywords = "Collinearity, Linear regression, Liu-type estimator, Particle swarm optimization, Ridge regression estimator",
author = "Deniz Inan and Erol Egrioglu and Busenur Sarica and Askin, {Oykum Esra} and Mujgan Tez",
year = "2017",
month = nov,
day = "17",
doi = "10.1080/03610926.2016.1267759",
language = "English",
volume = "46",
pages = "11358--11369",
journal = "Communications in Statistics - Theory and Methods",
issn = "0361-0926",
publisher = "Taylor and Francis Ltd.",
number = "22",

}

RIS

TY - JOUR

T1 - Particle swarm optimization based Liu-type estimator

AU - Inan, Deniz

AU - Egrioglu, Erol

AU - Sarica, Busenur

AU - Askin, Oykum Esra

AU - Tez, Mujgan

PY - 2017/11/17

Y1 - 2017/11/17

N2 - In this study, a new method for the estimation of the shrinkage and biasing parameters of Liu-type estimator is proposed. Because k is kept constant and d is optimized in Liu’s method, a (k, d) pair is not guaranteed to be the optimal point in terms of the mean square error of the parameters. The optimum (k, d) pair that minimizes the mean square error, which is a function of the parameters k and d, should be estimated through a simultaneous optimization process rather than through a two-stage process. In this study, by utilizing a different objective function, the parameters k and d are optimized simultaneously with the particle swarm optimization technique.

AB - In this study, a new method for the estimation of the shrinkage and biasing parameters of Liu-type estimator is proposed. Because k is kept constant and d is optimized in Liu’s method, a (k, d) pair is not guaranteed to be the optimal point in terms of the mean square error of the parameters. The optimum (k, d) pair that minimizes the mean square error, which is a function of the parameters k and d, should be estimated through a simultaneous optimization process rather than through a two-stage process. In this study, by utilizing a different objective function, the parameters k and d are optimized simultaneously with the particle swarm optimization technique.

KW - Collinearity

KW - Linear regression

KW - Liu-type estimator

KW - Particle swarm optimization

KW - Ridge regression estimator

U2 - 10.1080/03610926.2016.1267759

DO - 10.1080/03610926.2016.1267759

M3 - Journal article

AN - SCOPUS:85028566027

VL - 46

SP - 11358

EP - 11369

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

IS - 22

ER -