Adaptive Regression

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Springer Science & Business Media, Apr 20, 2000 - Business & Economics - 177 pages
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Linear regression is an important area of statistics, theoretical or applied. There have been a large number of estimation methods proposed and developed for linear regression. Each has its own competitive edge but none is good for all purposes. This manuscript focuses on construction of an adaptive combination of two estimation methods. The purpose of such adaptive methods is to help users make an objective choice and to combine desirable properties of two estimators.
 

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Contents

II
1
III
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IV
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V
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VII
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VIII
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IX
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XXXII
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XXXIII
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XXXIX
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XL
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XXIX
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XXXI
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XLI
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LIV
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LVI
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LVII
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LVIII
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LIX
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LX
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LXI
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LXII
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Page 162 - Dodge, Y. (1984). Robust estimation of regression coefficients by minimizing a convex combination of least squares and least absolute deviations. Computational Statistics Quarterly, vol. 1, pp. 139-153. Field, CA, and EM Ronchetti (1991). An overview of small sample asymptotic«.

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About the author (2000)

Yadolah Dodge is Professor of Statistics and Operations Research at the University of Neuchatel, Switzerland.

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