A new perspective in functional EIV linear model: Part I

被引:3
|
作者
Al-Sharadqah, Ali [1 ]
机构
[1] East Carolina Univ, Dept Math, Greenville, NC 27858 USA
关键词
Bias elimination; Computer vision; Errors-in-variables models; Maximum likelihood estimator; Mean squared error; Simple linear regression; Small-noise model; ESTIMATOR;
D O I
10.1080/03610926.2016.1143009
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Simple linear regression in the functional errors-in-variables (EIV) model is revisited from a different perspective, where the problem is addressed by using the small-sigma model instead of large sample theory. A general analysis is developed to study the slope's estimator that minimizes a family of objective functions, of which the least-squares fit and the maximum likelihood estimator are minimizers of such special functions. General formulas for the higher-order terms of the bias, the variance, and the mean square error are derived. Accordingly, two efficient estimators are proposed after implementing the pre- and the post-bias elimination techniques. Numerical tests confirm the superiority of the proposed estimators over others.
引用
收藏
页码:7039 / 7062
页数:24
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