Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25957
Title: Best linear near unbiased estimation for nonlinear signal models via semi-infinite programming approach
Authors: Ling, BWK
Ho, CYF
Siu, WC 
Dai, Q
Keywords: Best linear near unbiased estimations
Dual parameterization
Nonlinear signal models
Semi-infinite programming
Issue Date: 2015
Publisher: Elsevier
Source: Computational statistics and data analysis, 2015, v. 88, 6040, p. 111-118 How to cite?
Journal: Computational Statistics and Data Analysis 
Abstract: When the exact unbiasedness condition is relaxed to a near unbiasedness condition, this short communication shows that the best linear near unbiased estimation problem is actually a semi-infinite programming problem. Our recently developed dual parameterization method is applied for solving the problem. Computer numerical simulation results show that the semi-infinite programming approach outperforms the least squares approach.
URI: http://hdl.handle.net/10397/25957
ISSN: 0167-9473
DOI: 10.1016/j.csda.2015.01.020
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