Iterative estimators of parameters in linear models with partially variant coefficients

Shaolin Hu; Karl Meinke; Rushan Chen; Ouyang Huajiang

International Journal of Applied Mathematics and Computer Science (2007)

  • Volume: 17, Issue: 2, page 179-187
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Hu, Shaolin, et al. "Iterative estimators of parameters in linear models with partially variant coefficients." International Journal of Applied Mathematics and Computer Science 17.2 (2007): 179-187. <http://eudml.org/doc/207830>.

@article{Hu2007,
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.},
author = {Hu, Shaolin, Meinke, Karl, Chen, Rushan, Huajiang, Ouyang},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {linear model; iterative algorithms; variant coefficients; parameter estimation},
language = {eng},
number = {2},
pages = {179-187},
title = {Iterative estimators of parameters in linear models with partially variant coefficients},
url = {http://eudml.org/doc/207830},
volume = {17},
year = {2007},
}

TY - JOUR
AU - Hu, Shaolin
AU - Meinke, Karl
AU - Chen, Rushan
AU - Huajiang, Ouyang
TI - Iterative estimators of parameters in linear models with partially variant coefficients
JO - International Journal of Applied Mathematics and Computer Science
PY - 2007
VL - 17
IS - 2
SP - 179
EP - 187
AB - 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.
LA - eng
KW - linear model; iterative algorithms; variant coefficients; parameter estimation
UR - http://eudml.org/doc/207830
ER -

References

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  1. Brown D.C. (1964): The Error Model Best Estimation Trajectory. - Tech. Rep. AD 602799: http://stinet.dtic.mil/oai/oai?&verb=getRecorg&metadataPrefix=html&identifier=AD0602799 
  2. Dodge Y. and Kova J. (2000): Adaptive Regression. - Berlin: Springer. 
  3. Draper N.R. and Smith H. (1981): Applied Regression Analysis. - New York: Wiley. Zbl0548.62046
  4. Eubank R., Chunfeng H., Maldonado Y., Naisyin W., Suojin W., Buchanan R.J. (2004): Smoothing spline estimation in varying coefficient models. - J. Roy. Stat. Soc. B, Vol.66, No.3, pp.653-667. Zbl1046.62033
  5. Fahrmeier L. and Tutz G.(2001): Multivariate Statistical Modeling Based on Generalized Linear Models. - Berlin: Springer. 
  6. Frank E. and Harrell J.(2002): Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis. - New York: Springer. Zbl0982.62063
  7. Graybill F.A. and Iyer H.K. (1994): Regression Analysis: Concepts and Applications. - Massachusetts: Duxbury Press. Zbl0868.62056
  8. Hu Shaolin and Sun Guoji (2001): Process Monitoring Technique and Applications. - Bejing: National Defense Industry Press. 
  9. Kala R. and Kłaczyński K. (1988): Recursive improvement of estimates in a Gauss-Markov model with linear restrictions. Canad. J. Stat., Vol.16, No.3, pp.301-305. Zbl0667.62050
  10. Rencher A. (2000): Linear Models in Statistics. - New York: Wiley Zbl0943.62061

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