Admissibility of Linear Estimators with Respect to Inequality Constraints Under Some Loss Functions

作者：Zhang, Shang-li; Qin, Hong^*; Wu, Chang-chun

来源：Acta Mathematicae Applicatae Sinica-English Series, 2017, 33(4): 1073-1082.

DOI：10.1007/s10255-017-0719-5

• growth curve model
• linear estimation
• admissibility
• incomplete ellipsoidal restriction

In this paper, we study the issue of admissibility of linear estimated functions of parameters in the multivariate linear model with respect to inequality constraints under a matrix loss and a matrix balanced loss. Under the matrix loss, when the model is not constrained, the results in the class of non-homogeneous linear estimators [Xie, 1989, Chinese Sci. Bull., 1148-1149; Xie, 1993, J. Multivariate Anal., 1071-1074] showed that the admissibility under the matrix loss and the trace loss is equivalent. However, when the model is constrained by the inequality constraints, we find this equivalency is not tenable, our result shows that the admissibility of linear estimator does not depend on the constraints again under this matrix loss, but it is contrary under the trace loss [Wu, 2008, Linear Algebra Appl., 2040-2048], and it is also relative to the constraints under another matrix loss [He, 2009, Linear Algebra Appl., 241-250]. Under the matrix balanced loss, the necessary and sufficient conditions that the linear estimators are admissible in the class of homogeneous and non-homogeneous linear estimators are obtained, respectively. These results will support the theory of admissibility on the linear model with inequality constraints.

• 出版日期2017-10
• 单位北京交通大学; 华中师范大学; 嘉兴学院