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Theory of Probability and Mathematical Statistics

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Orthogonal regression method for observations from a mixture


Authors: R. E. Maĭboroda, G. V. Navara and O. V. Sugakova
Translated by: S. V. Kvasko
Original publication: Teoriya Imovirnostei ta Matematichna Statistika, tom 99 (2018).
Journal: Theor. Probability and Math. Statist. 99 (2019), 169-188
MSC (2010): Primary 62G05, 62G20; Secondary 62J05
DOI: https://doi.org/10.1090/tpms/1088
Published electronically: February 27, 2020
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Abstract | References | Similar Articles | Additional Information

Abstract: A generalization of the orthogonal regression method is considered for estimating parameters of the simple linear regression model with errors in variables for observations from a mixture with varying concentrations. The consistency and asymptotic normality is proved for the estimators studied in the paper. The dispersion matrix is established.


References [Enhancements On Off] (What's this?)

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Additional Information

R. E. Maĭboroda
Affiliation: Department of Probability Theory, Statistics, and Actuarial Mathematics, Faculty for Mechanics and Mathematics, Kyiv Taras Shevchenko National University, Volodymyrs’ka Street, 64/13, Kyiv 01601, Ukraine
Email: mre@univ.kiev.ua

G. V. Navara
Affiliation: Department of Probability Theory, Statistics, and Actuarial Mathematics, Faculty for Mechanics and Mathematics, Kyiv Taras Shevchenko National University, Volodymyrs’ka Street, 64/13, Kyiv 01601, Ukraine
Email: mrswade111017@gmail.com

O. V. Sugakova
Affiliation: Department of Mathematics and Theoretical Radiophysics, Faculty for Radiophysics, Electronics, and Computer Systems, Kyiv Taras Shevchenko National University, Volodymyrs’ka Street, 64/13, Kyiv 01601, Ukraine
Email: sugak@univ.kiev.ua

DOI: https://doi.org/10.1090/tpms/1088
Keywords: Model of a mixture, orthogonal regression, method of generalized estimating equations
Received by editor(s): March 4, 2018
Published electronically: February 27, 2020
Article copyright: © Copyright 2020 American Mathematical Society