Linearized models with constraints of type I

Lubomír Kubáček

Applications of Mathematics (2003)

  • Volume: 48, Issue: 2, page 81-95
  • ISSN: 0862-7940

Abstract

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In nonlinear regression models with constraints a linearization of the model leads to a bias in estimators of parameters of the mean value of the observation vector. Some criteria how to recognize whether a linearization is possible is developed. In the case that they are not satisfied, it is necessary to decide whether some quadratic corrections can make the estimator better. The aim of the paper is to contribute to the solution of the problem.

How to cite

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Kubáček, Lubomír. "Linearized models with constraints of type I." Applications of Mathematics 48.2 (2003): 81-95. <http://eudml.org/doc/33137>.

@article{Kubáček2003,
abstract = {In nonlinear regression models with constraints a linearization of the model leads to a bias in estimators of parameters of the mean value of the observation vector. Some criteria how to recognize whether a linearization is possible is developed. In the case that they are not satisfied, it is necessary to decide whether some quadratic corrections can make the estimator better. The aim of the paper is to contribute to the solution of the problem.},
author = {Kubáček, Lubomír},
journal = {Applications of Mathematics},
keywords = {nonlinear regression model with constraints; 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 = {Linearized models with constraints of type I},
url = {http://eudml.org/doc/33137},
volume = {48},
year = {2003},
}

TY - JOUR
AU - Kubáček, Lubomír
TI - Linearized models with constraints of type I
JO - Applications of Mathematics
PY - 2003
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
VL - 48
IS - 2
SP - 81
EP - 95
AB - In nonlinear regression models with constraints a linearization of the model leads to a bias in estimators of parameters of the mean value of the observation vector. Some criteria how to recognize whether a linearization is possible is developed. In the case that they are not satisfied, it is necessary to decide whether some quadratic corrections can make the estimator better. The aim of the paper is to contribute to the solution of the problem.
LA - eng
KW - nonlinear regression model with constraints; linearization; quadratization; nonlinear regression model; linearization; quadratization
UR - http://eudml.org/doc/33137
ER -

References

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  8. Regression Models with a Weak Nonlinearity. Technical Reports, University of Stuttgart, 1998, pp. 1–64. (1998) 
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  11. Generalized Inverse of the Matrix and Its Applications, J.  Wiley, New York, 1971. (1971) MR0338013
  12. 10.7151/dmps.1018, Discuss. Math. Probab. Stat. 21 (2001), 21–48. (2001) MR1868926DOI10.7151/dmps.1018

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