Results 221 to 230 of about 556,024 (263)

Fuzzy least squares

Information Sciences, 1988
The author discusses three models of fuzzy linear regression function for triangular fuzzy numbers [Fuzzy numbers with triangular shapes, cf. \textit{D. Dubois} and \textit{H. Prade}, Int. J. Syst. Sci. 9, 613-626 (1978; Zbl 0383.94045)]. Formulas are deduced by the least-squares method.
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Least squares and total least squares methods in image restoration

1997
Image restoration is the process of removing or minimizing degradations (blur) in an image. Mathematically, it can be modeled as a discrete ill-posed problem Hf=g, where H is a matrix of large dimension representing the blurring phenomena, and g is a vector representing the observed image.
Julie Kamm, James G. Nagy
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Constrained least-squares

Computers & Graphics, 1978
Abstract The method of least-squares is intended to fit a function to a set of data points closely, so as to satisfy a particular criterion of closeness. The approximating function can be taken to be a linear combination of linearly independent functions of an independent variable.
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Collinearity and Total Least Squares

SIAM Journal on Matrix Analysis and Applications, 1994
The least squares (LS) and total least squares (TLS) methods are commonly used to solve the overdetermined system of equations \(Ax\approx b\). The main objective of this paper is to examine TLS when \(A\) is nearly rank deficient by outlining its differences and similarities to the well-known truncated LS method.
Ricardo D. Fierro, James R. Bunch
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Maximum likelihood, least squares, and penalized least squares for PET

IEEE Transactions on Medical Imaging, 1993
The EM algorithm is the basic approach used to maximize the log likelihood objective function for the reconstruction problem in positron emission tomography (PET). The EM algorithm is a scaled steepest ascent algorithm that elegantly handles the nonnegativity constraints of the problem. It is shown that the same scaled steepest descent algorithm can be
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Method of least squares

2009
The method of least squares controls the flow of errors via the elements of the design matrix. Hence, assuming linear systems with differing design matrices aiming at the same set of unknowns, the adjustment’s uncertainties would differ even if the uncertainties of the input data were the same.
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Least Squares or Least Circles?

CHANCE, 2010
(2010). Least Squares or Least Circles? CHANCE: Vol. 23, Collecting Data in Challenging Settings, pp. 38-42.
Ivo Petras, Igor Podlubny
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The Method of Least Squares

1981
In the next three chapters we shall discuss a particular form of statistical model, which gives rise to simple statistical methods of very wide applicability. The basic model has been mentioned in Section 2.1, Equation (2.2), see also Example 3.12, but the following example illustrates how it arises in practice.
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Least Squares Sign-Solvability

SIAM Journal on Matrix Analysis and Applications, 1995
The author constructs a family of least squares sign-solvable linear systems from the vertex-incidence matrices of trees, and develops their general properties. The structure of a least squares sign-solvable system is shown to be analogous to that of sign-solvable linear systems.
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Review of Ordinary Least Squares and Generalized Least Squares

1984
The purpose of this chapter is to review the fundamentals of ordinary least squares and generalized least squares in the context of linear regression analysis. The presentation here is somewhat condensed given our objective of focusing on more advanced topics in econometrics.
Thomas B. Fomby, Stanley R. Johnson, R. Carter Hill   +2 more
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