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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Notes
- 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
- 3.
For example, see: http://www.math.fsu.edu/Virtual/index.php?f=21
References
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
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Di Lorenzo, R. (2013). Generalizations. In: Trading Systems. Perspectives in Business Culture. Springer, Milano. https://doi.org/10.1007/978-88-470-2706-0_12
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DOI: https://doi.org/10.1007/978-88-470-2706-0_12
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