Orthogonality and Linear Sufficiency in Partitioned and Reduced Linear Models

The partitioned linear model ℳ 12 is considered together with the reduced counterparts ℳ 2 and ℳ (2) , which are free from nuisance parameters present in ℳ 12 . The conditions are established under which the best linear unbiased estimator of parametric functions in the reduced model ℳ 2 (ℳ (2) ) pre...

Full description

Saved in:
Bibliographic Details
Published in:Communications in statistics. Theory and methods 2011-01, Vol.40 (6), p.1124-1130
Main Author: Pordzik, Paweł R.
Format: Article
Language:English
Subjects:
BLUE
Estimators
Exact sciences and technology
Gauss-Markov model
General topics
Linear sufficiency
Mathematical models
Mathematics
Nuisance
Nuisance parameters
Optimization
Orthogonality
Preserves
Probability and statistics
Sciences and techniques of general use
Statistics
Studies
Sufficiency and information
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The partitioned linear model ℳ 12 is considered together with the reduced counterparts ℳ 2 and ℳ (2) , which are free from nuisance parameters present in ℳ 12 . The conditions are established under which the best linear unbiased estimator of parametric functions in the reduced model ℳ 2 (ℳ (2) ) preserves its optimality in ℳ 12 . Expressed in terms of orthogonality and linear sufficiency, the characterizations remain valid when the support of the reduced model ℳ 2 (ℳ (2) ) is a proper subset of the support of ℳ 12 . Moreover, some relationships between orthogonality and linear sufficiency are presented.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610920903537319