The asymptotic normality of an adjusted least squares estimator in a multivariate vector errors-in-variables regression model
Author:
I. O. Sen’ko
Translated by:
N. Semenov
Journal:
Theor. Probability and Math. Statist. 88 (2014), 175-190
MSC (2010):
Primary 62J12
DOI:
https://doi.org/10.1090/S0094-9000-2014-00929-1
Published electronically:
July 24, 2014
MathSciNet review:
3112643
Full-text PDF Free Access
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Abstract: An adjusted least squares estimator in a linear multivariate vector error-in-variables regression model is considered in this paper. Conditions for the asymptotic normality of this estimator are given. A modification of the estimator is constructed whose asymptotic properties are the same as those of the adjusted least squares estimator and which is stable even if a sample is small.
References
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References
- R. J. Carrol, D. Ruppert, and L. A. Sefanski, Measurement Error in Nonlinear Models, Monographs on Statistics and Applied Probability, vol. 63, Chapman & Hall/CRC, London, 1995. MR 1630517 (2000c:62001)
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- W. A. Fuller, Measurement Error Models, Wiley, New York, 1987. MR 898653 (89a:62160)
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Additional Information
I. O. Sen’ko
Affiliation:
Department of Mathematical Analysis, Faculty for Mechanics and Mathematics, National Taras Shevchenko University, Volodymyrs’ka Street, 64, Kyiv 01601, Ukraine
Email:
ivan_senko@ukr.net
Keywords:
Error-in-variables models,
adjusted least squares estimator,
asymptotic normality,
small samples
Received by editor(s):
October 25, 2012
Published electronically:
July 24, 2014
Article copyright:
© Copyright 2014
American Mathematical Society