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Published in:
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2013 | OriginalPaper | Chapter

12. Generalizations

Author : Renato Di Lorenzo

Published in: Trading Systems

Publisher: Springer Milan

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Abstract

The ideas underlying the method of least squares and of the ordinary regression can be generalized using any family of fitting functions chosen wisely.

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previous chapter Process of the AR(p) Type
next chapter The Complete Open-Loop Scheme
Footnotes
1
As mentioned earlier, our particular criterion of optimality will be specified and chosen from among several possible ones.
 
2
Provided they are linearly independent, that is, if no one can be expressed as a linear combination of the others. See: http://​en.​wikipedia.​org/​wiki/​Linear_​independence
 
Literature
Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis. Prentice Hall, Englewood Cliffs
Brown RG (1963) Smoothing, forecasting and prediction. Prentice Hall, Englewood Cliffs
Spiegel MR (1975) Probability and statistics. McGraw-Hill, New York
Wylie CR (1960) Advanced engineering mathematics. McGraw-Hill, New York
Metadata
Title
Generalizations
Author
Renato Di Lorenzo
Copyright Year
2013
Publisher
Springer Milan
Book
Trading Systems

Print ISBN: 978-88-470-2705-3

Electronic ISBN: 978-88-470-2706-0

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-88-470-2706-0_12