How to deal with regression models with a weak nonlinearity
Discussiones Mathematicae. Probability and Statistics, Tome 21 (2001) no. 1, pp. 21-48
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If a nonlinear regression model is linearized in a non-sufficient small neighbourhood of the actual parameter, then all statistical inferences may be deteriorated. Some criteria how to recognize this are already developed. The aim of the paper is to demonstrate the behaviour of the program for utilization of these criteria.
Keywords:
nonlinear regression model, criteria of linearization, demo program
@article{DMPS_2001_21_1_a2,
author = {Tesar{\'\i}kov\'a, Eva and Kub\'a\v{c}ek, Lubom{\'\i}r},
title = {How to deal with regression models with a weak nonlinearity},
journal = {Discussiones Mathematicae. Probability and Statistics},
pages = {21--48},
publisher = {mathdoc},
volume = {21},
number = {1},
year = {2001},
language = {en},
url = {http://geodesic.mathdoc.fr/item/DMPS_2001_21_1_a2/}
}
TY - JOUR AU - Tesaríková, Eva AU - Kubáček, Lubomír TI - How to deal with regression models with a weak nonlinearity JO - Discussiones Mathematicae. Probability and Statistics PY - 2001 SP - 21 EP - 48 VL - 21 IS - 1 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/DMPS_2001_21_1_a2/ LA - en ID - DMPS_2001_21_1_a2 ER -
Tesaríková, Eva; Kubáček, Lubomír. How to deal with regression models with a weak nonlinearity. Discussiones Mathematicae. Probability and Statistics, Tome 21 (2001) no. 1, pp. 21-48. http://geodesic.mathdoc.fr/item/DMPS_2001_21_1_a2/