Robust estimation of nonlinear structural models

The problem is considered of identification of nonlinear error-in-variables models under conditions of a heavy contamination of the sample, including the presence of outliers (i.e., anomalous observations). On the basis of robust estimation methods, we propose a development of the algorithms of adju...

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Bibliographic Details
Published in:Journal of applied and industrial mathematics 2014, Vol.8 (1), p.28-39
Main Authors: Denisov, V. I., Timofeeva, A. Yu, Khailenko, E. A., Buzmakova, O. I.
Format: Article
Language:English
Subjects:
Algorithms
Applied mathematics
Approximation
Budgeting
Contamination
Estimates
Households
Least squares method
Mathematical models
Mathematical problems
Mathematics
Mathematics and Statistics
Nonlinearity
Regularity
Studies
Variables
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Summary:The problem is considered of identification of nonlinear error-in-variables models under conditions of a heavy contamination of the sample, including the presence of outliers (i.e., anomalous observations). On the basis of robust estimation methods, we propose a development of the algorithms of adjusted and total least squares. This has enabled us to improve the accuracy of the response prediction in the presence of outliers in the sample. The algorithms are used for constructing the Engel curve from the budget survey data. In result, we draw better conclusions about the regularities of the household behavior when the income changes.
ISSN:1990-4789
1990-4797
DOI:10.1134/S1990478914010049