Iterative Estimators of Parameters in Linear Models with Partially Variant Coefficients

Authors:
Shaolin Hu;Karl Meinke;Rushan Chen;Ouyang Huajiang
Affiliations:
-;Department of Computer Science, Royal Institute of Technology, Stockholm, 100-44, Sweden;Nanjing University of Science and Technology, 210071, Nanjing, China;University of Liverpool, Liverpool, United Kingdom, L69 3 GH, Liverpool, UK
Venue:
International Journal of Applied Mathematics and Computer Science
Year:
2007

Citing 1
Cited 1

Regression Modeling Strategies

Regression Modeling Strategies

The steady-state impedance operator of a linear periodically time-varying one-port network and its determination

International Journal of Applied Mathematics and Computer Science - Special Section: Robot Control Theory Cezary Zielinski

Quantified Score

Hi-index	0.00

Visualization

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.