Abstract
In recent years some alternatives to the least squares estimation function in the linear model have been introduced. Most of these functions can be represented as a linear or non-linear transformation of the least squares function. It is shown that these estimation functions can be derived as minimum norm estimation functions in the class of linear transforms of the least squares estimation function. In addition a numerical classification of these functions is given which yields an appropriate algorithm for computation.
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© 1989 Springer-Verlag Berlin · Heidelberg
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Müller, P. (1989). Numerical Classification of Biased Estimators. In: Optiz, O. (eds) Conceptual and Numerical Analysis of Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-75040-3_12
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DOI: https://doi.org/10.1007/978-3-642-75040-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-51641-5
Online ISBN: 978-3-642-75040-3
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