A new perspective in functional EIV linear model: Part I

Ali Al-Sharadqah

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 14, 7039-7062

Abstract: 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.

Date: 2017
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DOI: 10.1080/03610926.2016.1143009

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