Previous |  Up |  Next

Article

Keywords:
admissibility; extended balanced loss function; linear admissible estimator
Summary:
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.
References:
[1] Baksalary, J. K., Markiewicz, A.: Admissible linear estimators in restricted linear models. Linear Algebra Appl. 70 (1985), 9-19. DOI 
[2] Baksalary, J. K., Markiewicz, A.: Characterizations of admissible linear estimators in restricted linear models. Statist. Planning Inference 13 (1986), 395-398. DOI 
[3] Baksalary, J. K., Markiewicz, A.: Admissible linear estimators in the general Gauss-Markov model. Statist. Planning Inference 19 (1988), 349-359. DOI  | Zbl 0656.62076
[4] Cohen, A.: All admissible linear estimates of the mean vector. Ann. Math. Statist. 37 (1966), 458-463. DOI 
[5] Chen, X. R., Chen, G., Wu, Q., Zhao, L.: Parameter Estimation Theory for Linear Models. Science Press, Beijing 1985.
[6] Chaturvedi, A., Shalabh: Bayesian estimation of regression coefficients under extended balanced loss function. Comm. Statist. Theory and Methods 43 (2014), 4253-4264. DOI 
[7] Cao, M.: Admissibility of linear estimators for the stochastic regression coefficient in a general Gauss-Markoff model under a balanced loss function. Multivariate Analysis 124 (2014), 25-30. DOI 
[8] Cao, M., He, D.: Linearly admissible estimators on linear functions of regression coefficient under balanced loss function. Comm. Statist. Theory and Methods 48 (2019), 2700-2706. DOI 
[9] Dong, L., Wu, Q.: Necessary and sufficient conditions for linear estimators of stochastic regression coefficients and parameters to be admissible under quadratic loss. Acta Math. Sinica 31 (1988), 145-157.
[10] Graybill, F. A.: Matrices With Application in Statistics. Californie 1983.
[11] Gro{\ss}, J.: Linear Regression. Springer-Verlag, Berlin Heidelberg 2003.
[12] Gross, J., Markiewicz, A.: Characterizations of admissible linear estimators in the linear model. Linear Algebra Appl. 388 (2004), 239-248. DOI 
[13] Hoffmann, K.: All admissible linear estimators of the regression parameter vector in the case of an arbitrary parameter subset. Statist. Planning Inference 48 (1995), 371-377. DOI 
[14] Klonecki, W., Zontek, S.: On the structure of admissible linear estimators. Multivariate Analysis 24 (1998), 11-30. DOI 10.1016/0047-259X(88)90098-X | Zbl 0664.62008
[15] Kaçiranlar, S., Dawoud, I.: The optimal extended balanced loss function estimators. Comput. Appl. Math. 345 (2019), 86-98. DOI 10.1016/j.cam.2018.06.021
[16] Lehmann, E. L., Casella, G.: Theory of Point Estimation. Second edition. Springer-Verlag, New York 2005.
[17] Markiewicz, A.: Characterization of general ridge estimators. Statist. Probab. Lett. 27 (1996), 145-148. DOI 10.1016/0167-7152(95)00056-9
[18] Özbay, N., Kaçiranlar, S.: The performance of the adaptive optimal estimator under the extended balanced loss function. Comm. Statist. Theory and Methods 46 (2017), 11315-11326. DOI 
[19] Rao, C. R.: Estimation of parameters in a linear model. Ann. Statist. 4 (1976), 1023-1037. Zbl 0421.62047
[20] St\k{e}pniak, C.: Admissible linear estimators in mixed linear models. J. Multivariate Analysis 31 (1989), 90-106. DOI 
[21] Shalabh, S.: Performance of Stein - rule Procedure for simultaneous prediction of actual and average values of study variable in linear regression model. Bull. Int. Statist. Inst. 56 (1995), 1375-1390.
[22] Shalabh, S., Toutenburg, H., Heumann, C.: Stein-rule estimation under an extended balanced loss function. J. Statist. Comput. Simul. 79 (2009), 1259-1273. DOI 
[23] St\k{e}pniak, C.: Admissible invariant estimators in a linear model. Kybernetika 50 (2014), 310-321.
[24] Synówka-Bejenka, E., Zontek, S.: On admissibility of linear estimators in models with finitely generated parameter space. Kybernetika (2016), 724-734.
[25] Xu, X., Wu, Q.: Linearly admissible estimators of regression coefficient under balanced loss. Acta Math. Sci. 20 (2000), 468-473.
[26] Lu, C. Y., Shi, N. Z.: Admissible linear estimators in linear models with respect to inequality constraints. Linear Algebra Appl. 354 (2002), 187-194. DOI 
[27] Zellner, A.: Bayesian and Non-Bayesian Estimation Using Balanced Loss Functions. Statistical Decision Theory and Related Topics V, Springer, New York 1994, pp. 377-390.
Partner of
EuDML logo