Linear versus quadratic estimators in linearized models

Lubomír Kubáček

Applications of Mathematics (2004)

  • Volume: 49, Issue: 2, page 81-95
  • ISSN: 0862-7940

Abstract

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In nonlinear regression models an approximate value of an unknown parameter is frequently at our disposal. Then the linearization of the model is used and a linear estimate of the parameter can be calculated. Some criteria how to recognize whether a linearization is possible are developed. In the case that they are not satisfied, it is necessary to take into account either some quadratic corrections or to use the nonlinear least squares method. The aim of the paper is to find some criteria for an ordering linear and quadratic estimators.

How to cite

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Kubáček, Lubomír. "Linear versus quadratic estimators in linearized models." Applications of Mathematics 49.2 (2004): 81-95. <http://eudml.org/doc/33176>.

@article{Kubáček2004,
abstract = {In nonlinear regression models an approximate value of an unknown parameter is frequently at our disposal. Then the linearization of the model is used and a linear estimate of the parameter can be calculated. Some criteria how to recognize whether a linearization is possible are developed. In the case that they are not satisfied, it is necessary to take into account either some quadratic corrections or to use the nonlinear least squares method. The aim of the paper is to find some criteria for an ordering linear and quadratic estimators.},
author = {Kubáček, Lubomír},
journal = {Applications of Mathematics},
keywords = {nonlinear regression model; linearization; quadratization; nonlinear regression model; linearization; quadratization},
language = {eng},
number = {2},
pages = {81-95},
publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},
title = {Linear versus quadratic estimators in linearized models},
url = {http://eudml.org/doc/33176},
volume = {49},
year = {2004},
}

TY - JOUR
AU - Kubáček, Lubomír
TI - Linear versus quadratic estimators in linearized models
JO - Applications of Mathematics
PY - 2004
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
VL - 49
IS - 2
SP - 81
EP - 95
AB - In nonlinear regression models an approximate value of an unknown parameter is frequently at our disposal. Then the linearization of the model is used and a linear estimate of the parameter can be calculated. Some criteria how to recognize whether a linearization is possible are developed. In the case that they are not satisfied, it is necessary to take into account either some quadratic corrections or to use the nonlinear least squares method. The aim of the paper is to find some criteria for an ordering linear and quadratic estimators.
LA - eng
KW - nonlinear regression model; linearization; quadratization; nonlinear regression model; linearization; quadratization
UR - http://eudml.org/doc/33176
ER -

References

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