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Iterative estimators of parameters in linear models with partially variant coefficients
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Title: Iterative estimators of parameters in linear models with partially variant coefficients

Creator:

Hu, Shaolin ; Meinke, Karl ; Chen, Rushan ; Huajiang, Ouyang

Date:

2007

Resource Type:

artykuł

Contributor:

Korbicz, Józef (1951- ) - red.

Group publication title:

AMCS, volume 17 (2007)

Abstract:

A new kind of linear model with partially variant coefficients is proposed and a series of iterative algorithms are introduced and verified. The new generalized linear model includes the ordinary linear regression model as a special case. The iterative algorithms efficiently overcome some difficulties in computation with multidimensional inputs and incessantly appending parameters. An important application is described at the end of this article, which shows that this new model is reasonable and applicable in practical fields.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:78647

DOI:

10.2478/v10006-007-0017-0

Pages:

179-187

Source:

AMCS, volume 17, number 2 (2007) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

linear model parameter estimation iterative algorithms variant coefficients

Object collections:

Last modified:

Jul 14, 2025

In our library since:

Apr 2, 2024

Number of object content hits:

115

All available object's versions:

https://zbc.uz.zgora.pl/repozytorium/publication/88401

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Iterative estimators of parameters in linear models with partially variant coefficients Jul 14, 2025

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