A new perspective in functional EIV linear model: Part I

作者：Al Sharadqah Ali^*

来源：Communications in Statistics - Theory and Methods, 2017, 46(14): 7039-7062.

DOI：10.1080/03610926.2016.1143009

• Bias elimination
• Computer vision
• Errors-in-variables models
• Maximum likelihood estimator
• Mean squared error
• Simple linear regression
• Small-noise model

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.

• 出版日期2017
• 单位East Carolina