Characterization of admissible linear estimators under extended balanced loss function

Buatikan Mirezi; Selahattin Kaçıranlar

Kybernetika (2021)

  • Volume: 57, Issue: 4, page 613-627
  • ISSN: 0023-5954

Abstract

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In this paper, we study the admissibility of linear estimator of regression coefficient in linear model under the extended balanced loss function (EBLF). The sufficient and necessary condition for linear estimators to be admissible are obtained respectively in homogeneous and non-homogeneous classes. Furthermore, we show that admissible linear estimator under the EBLF is a convex combination of the admissible linear estimator under the sum of square residuals and quadratic loss function.

How to cite

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Mirezi, Buatikan, and Kaçıranlar, Selahattin. "Characterization of admissible linear estimators under extended balanced loss function." Kybernetika 57.4 (2021): 613-627. <http://eudml.org/doc/298017>.

@article{Mirezi2021,
abstract = {In this paper, we study the admissibility of linear estimator of regression coefficient in linear model under the extended balanced loss function (EBLF). The sufficient and necessary condition for linear estimators to be admissible are obtained respectively in homogeneous and non-homogeneous classes. Furthermore, we show that admissible linear estimator under the EBLF is a convex combination of the admissible linear estimator under the sum of square residuals and quadratic loss function.},
author = {Mirezi, Buatikan, Kaçıranlar, Selahattin},
journal = {Kybernetika},
keywords = {admissibility; extended balanced loss function; linear admissible estimator},
language = {eng},
number = {4},
pages = {613-627},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Characterization of admissible linear estimators under extended balanced loss function},
url = {http://eudml.org/doc/298017},
volume = {57},
year = {2021},
}

TY - JOUR
AU - Mirezi, Buatikan
AU - Kaçıranlar, Selahattin
TI - Characterization of admissible linear estimators under extended balanced loss function
JO - Kybernetika
PY - 2021
PB - Institute of Information Theory and Automation AS CR
VL - 57
IS - 4
SP - 613
EP - 627
AB - In this paper, we study the admissibility of linear estimator of regression coefficient in linear model under the extended balanced loss function (EBLF). The sufficient and necessary condition for linear estimators to be admissible are obtained respectively in homogeneous and non-homogeneous classes. Furthermore, we show that admissible linear estimator under the EBLF is a convex combination of the admissible linear estimator under the sum of square residuals and quadratic loss function.
LA - eng
KW - admissibility; extended balanced loss function; linear admissible estimator
UR - http://eudml.org/doc/298017
ER -

References

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