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A note on adaptive generalized ridge regression estimator

Publication ,  Journal Article
Wang, SG; Chow, SC
Published in: Statistics and Probability Letters
January 1, 1990

The problem of estimating parameters in a linear regression model is considered. A class of adaptive generalized ridge estimator is proposed. It is shown that the proposed estimator has smaller mean squared error than the least squares estimator under some mild conditions on the constants that involved in the ridge parameters. © 1990.

Duke Scholars

Published In

Statistics and Probability Letters

DOI

ISSN

0167-7152

Publication Date

January 1, 1990

Volume

10

Issue

1

Start / End Page

17 / 21

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0102 Applied Mathematics
 

Citation

APA
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ICMJE
MLA
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Wang, S. G., & Chow, S. C. (1990). A note on adaptive generalized ridge regression estimator. Statistics and Probability Letters, 10(1), 17–21. https://doi.org/10.1016/0167-7152(90)90106-H
Wang, S. G., and S. C. Chow. “A note on adaptive generalized ridge regression estimator.” Statistics and Probability Letters 10, no. 1 (January 1, 1990): 17–21. https://doi.org/10.1016/0167-7152(90)90106-H.
Wang SG, Chow SC. A note on adaptive generalized ridge regression estimator. Statistics and Probability Letters. 1990 Jan 1;10(1):17–21.
Wang, S. G., and S. C. Chow. “A note on adaptive generalized ridge regression estimator.” Statistics and Probability Letters, vol. 10, no. 1, Jan. 1990, pp. 17–21. Scopus, doi:10.1016/0167-7152(90)90106-H.
Wang SG, Chow SC. A note on adaptive generalized ridge regression estimator. Statistics and Probability Letters. 1990 Jan 1;10(1):17–21.
Journal cover image

Published In

Statistics and Probability Letters

DOI

ISSN

0167-7152

Publication Date

January 1, 1990

Volume

10

Issue

1

Start / End Page

17 / 21

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0102 Applied Mathematics