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

Original publication:
Teoriya Imovirnostei ta Matematichna Statistika, tom **88** (2013).

Journal:
Theor. Probability and Math. Statist. **88** (2014), 175-190

MSC (2010):
Primary 62J12

Published electronically:
July 24, 2014

MathSciNet review:
3112643

Full-text PDF

Abstract | References | Similar Articles | Additional Information

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.

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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{\textunderscore}senko@ukr.net

DOI:
https://doi.org/10.1090/S0094-9000-2014-00929-1

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