# On decompositions of estimators under a general linear model with partial parameter restrictions

Bo Jiang; Yongge Tian; Xuan Zhang

Open Mathematics (2017)

- Volume: 15, Issue: 1, page 1300-1322
- ISSN: 2391-5455

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topBo Jiang, Yongge Tian, and Xuan Zhang. "On decompositions of estimators under a general linear model with partial parameter restrictions." Open Mathematics 15.1 (2017): 1300-1322. <http://eudml.org/doc/288393>.

@article{BoJiang2017,

abstract = {A general linear model can be given in certain multiple partitioned forms, and there exist submodels associated with the given full model. In this situation, we can make statistical inferences from the full model and submodels, respectively. It has been realized that there do exist links between inference results obtained from the full model and its submodels, and thus it would be of interest to establish certain links among estimators of parameter spaces under these models. In this approach the methodology of additive matrix decompositions plays an important role to obtain satisfactory conclusions. In this paper, we consider the problem of establishing additive decompositions of estimators in the context of a general linear model with partial parameter restrictions. We will demonstrate how to decompose best linear unbiased estimators (BLUEs) under the constrained general linear model (CGLM) as the sums of estimators under submodels with parameter restrictions by using a variety of effective tools in matrix analysis. The derivation of our main results is based on heavy algebraic operations of the given matrices and their generalized inverses in the CGLM, while the whole contributions illustrate various skillful uses of state-of-the-art matrix analysis techniques in the statistical inference of linear regression models.},

author = {Bo Jiang, Yongge Tian, Xuan Zhang},

journal = {Open Mathematics},

keywords = {Partitioned linear model; Submodel; Parameter restriction; BLUE; Additive matrix decomposition},

language = {eng},

number = {1},

pages = {1300-1322},

title = {On decompositions of estimators under a general linear model with partial parameter restrictions},

url = {http://eudml.org/doc/288393},

volume = {15},

year = {2017},

}

TY - JOUR

AU - Bo Jiang

AU - Yongge Tian

AU - Xuan Zhang

TI - On decompositions of estimators under a general linear model with partial parameter restrictions

JO - Open Mathematics

PY - 2017

VL - 15

IS - 1

SP - 1300

EP - 1322

AB - A general linear model can be given in certain multiple partitioned forms, and there exist submodels associated with the given full model. In this situation, we can make statistical inferences from the full model and submodels, respectively. It has been realized that there do exist links between inference results obtained from the full model and its submodels, and thus it would be of interest to establish certain links among estimators of parameter spaces under these models. In this approach the methodology of additive matrix decompositions plays an important role to obtain satisfactory conclusions. In this paper, we consider the problem of establishing additive decompositions of estimators in the context of a general linear model with partial parameter restrictions. We will demonstrate how to decompose best linear unbiased estimators (BLUEs) under the constrained general linear model (CGLM) as the sums of estimators under submodels with parameter restrictions by using a variety of effective tools in matrix analysis. The derivation of our main results is based on heavy algebraic operations of the given matrices and their generalized inverses in the CGLM, while the whole contributions illustrate various skillful uses of state-of-the-art matrix analysis techniques in the statistical inference of linear regression models.

LA - eng

KW - Partitioned linear model; Submodel; Parameter restriction; BLUE; Additive matrix decomposition

UR - http://eudml.org/doc/288393

ER -

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