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BibTeX entry
@Article{Palancz:2014:FDD,
author = "B{\'e}la Pal{\'a}ncz",
title = "Fitting Data with Different Error Models",
journal = j-MATHEMATICA-J,
volume = "16",
number = "??",
pages = "??--??",
month = "????",
year = "2014",
CODEN = "????",
ISSN = "1047-5974 (print), 1097-1610 (electronic)",
bibdate = "Wed Sep 10 10:37:47 MDT 2014",
bibsource = "http://www.mathematica-journal.com/issue/v0i0/;
http://www.math.utah.edu/pub/tex/bib/mathematicaj.bib",
URL = "http://www.mathematica-journal.com/2014/04/fitting-data-with-different-error-models/",
abstract = "A maximum likelihood estimator has been applied to
find regression parameters of a straight line in case
of different error models. Assuming Gaussian-type noise
for the measurement errors, explicit results for the
parameters can be given employing Mathematica. In the
case of the ordinary least squares ( ), total least
squares ( ), and least geometric mean deviation ( )
approaches, as well as the error model of combining
ordinary least squares ( and ) in the Pareto sense,
simple formulas are given to compute the parameters via
a reduced Gr{\"o}bner basis. Numerical examples
illustrate the methods, and the results are checked via
direct global minimization of the residuals. \ldots{}",
acknowledgement = ack-nhfb,
}
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