On the Equivalence between Orthogonal Regression and Linear Model with Type-II Constraints
Sandra Donevska; Eva Fišerová; Karel Hron
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica (2011)
- Volume: 50, Issue: 2, page 19-27
- ISSN: 0231-9721
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topDonevska, Sandra, Fišerová, Eva, and Hron, Karel. "On the Equivalence between Orthogonal Regression and Linear Model with Type-II Constraints." Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica 50.2 (2011): 19-27. <http://eudml.org/doc/196302>.
@article{Donevska2011,
abstract = {Orthogonal regression, also known as the total least squares method, regression with errors-in variables or as a calibration problem, analyzes linear relationship between variables. Comparing to the standard regression, both dependent and explanatory variables account for measurement errors. Through this paper we shortly discuss the orthogonal least squares, the least squares and the maximum likelihood methods for estimation of the orthogonal regression line. We also show that all mentioned approaches lead to the same estimates in a special case.},
author = {Donevska, Sandra, Fišerová, Eva, Hron, Karel},
journal = {Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica},
keywords = {linear regression model with type-II constraints; orthogonal regression; estimation; estimation},
language = {eng},
number = {2},
pages = {19-27},
publisher = {Palacký University Olomouc},
title = {On the Equivalence between Orthogonal Regression and Linear Model with Type-II Constraints},
url = {http://eudml.org/doc/196302},
volume = {50},
year = {2011},
}
TY - JOUR
AU - Donevska, Sandra
AU - Fišerová, Eva
AU - Hron, Karel
TI - On the Equivalence between Orthogonal Regression and Linear Model with Type-II Constraints
JO - Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
PY - 2011
PB - Palacký University Olomouc
VL - 50
IS - 2
SP - 19
EP - 27
AB - Orthogonal regression, also known as the total least squares method, regression with errors-in variables or as a calibration problem, analyzes linear relationship between variables. Comparing to the standard regression, both dependent and explanatory variables account for measurement errors. Through this paper we shortly discuss the orthogonal least squares, the least squares and the maximum likelihood methods for estimation of the orthogonal regression line. We also show that all mentioned approaches lead to the same estimates in a special case.
LA - eng
KW - linear regression model with type-II constraints; orthogonal regression; estimation; estimation
UR - http://eudml.org/doc/196302
ER -
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