Linear Least Squares

Richard L. Branham
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Richard L. Branham: Jefe Area Matematicas y Del Centro Regional de Investigaciones Científicas y Tecnológicas

Chapter Chapter 5 in Scientific Data Analysis, 1990, pp 84-132 from Springer

Abstract: Abstract In the previous chapter we talked, in general, about the method of least squares and, in particular, the mathematical justification for selecting it over other criteria. In this chapter we consider practical methods for analyzing a least squares problem. Equations (4.4)–(4.8) present a brief derivation, by calculus, of the normal equations, still the most popular—but by no means only—way of solving least squares problems; in Section 5.4 we shall see that orthogonal transformations allow us to obtain a least squares solution without forming normal equations, a procedure that offers certain advantages but also suffers from some drawbacks, something that proponents of orthogonal transformations frequently overlook.

Keywords: Condition Number; Normal Equation; Gaussian Elimination; Orthogonal Transformation; Mean Absolute Deviation (search for similar items in EconPapers)
Date: 1990
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DOI: 10.1007/978-1-4612-3362-6_5

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