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→See also: completing merge, mention Lowess, though in some circles it seems to be considered a subset of this definition |
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The trade-off for these features is increased computation. Because it is so computationally intensive, LOESS would have been practically impossible to use in the era when least squares regression was being developed. Most other modern methods for process modeling are similar to LOESS in this respect. These methods have been consciously designed to use our current computational ability to the fullest possible advantage to achieve goals not easily achieved by traditional approaches.
Plotting a smooth curve through a set of data points using this statistical technique is called a '''Loess Curve''', particularily when each smoothed value is given by a weighted quadratic least squares regression over the span of values of the y-axis [[scattergram]] criterion variable. When each smoothed value is given by a weighted linear least squares regression over the span, this is known as a '''Lowess curve'''.
==Definition of a LOESS model==
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==See also==
*[[Non-parametric statistics]]
==External links==
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*[http://www.itl.nist.gov/div898/handbook/pmd/section1/pmd144.htm NIST Engineering Statistics Handbook Section on LOESS]
*[http://www.stat.purdue.edu/~wsc/localfitsoft.html Local Fitting Software]
*[http://www.mathworks.com/access/helpdesk/help/toolbox/curvefit/ch_data7.html Lowess and Loess: Local Regression Smoothing]
*[http://stat.ethz.ch/R-manual/R-patched/library/stats/html/lowess.html Scatter Plot Smoothing]
{{NIST-PD}}
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