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- Local_regression abstract "LOESS and LOWESS (locally weighted scatterplot smoothing) are two strongly related non-parametric regression methods that combine multiple regression models in a k-nearest-neighbor-based meta-model. "LOESS" is a later generalization of LOWESS; although it is not a true initialism, it may be understood as standing for "LOcal regrESSion".LOESS and LOWESS thus build on "classical" methods, such as linear and nonlinear least squares regression. They address situations in which the classical procedures do not perform well or cannot be effectively applied without undue labor. LOESS combines much of the simplicity of linear least squares regression with the flexibility of nonlinear regression. It does this by fitting simple models to localized subsets of the data to build up a function that describes the deterministic part of the variation in the data, point by point. In fact, one of the chief attractions of this method is that the data analyst is not required to specify a global function of any form to fit a model to the data, only to fit segments of the data.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.A smooth curve through a set of data points obtained with this statistical technique is called a Loess Curve, particularly 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; however, some authorities treat Lowess and Loess as synonyms.".
- Local_regression thumbnail Loess_curve.svg?width=300.
- Local_regression wikiPageExternalLink ?hp.
- Local_regression wikiPageExternalLink loess-smoothing-in-excel.
- Local_regression wikiPageExternalLink loess.htm.
- Local_regression wikiPageExternalLink lowess.html.
- Local_regression wikiPageExternalLink Comments.aspx?ArticleId=28&ArticleName=Electoral+Projections+Using+LOESS.
- Local_regression wikiPageExternalLink pmd144.htm.
- Local_regression wikiPageExternalLink quantile-loess-combining-a-moving-quantile-window-with-loess-r-function.
- Local_regression wikiPageExternalLink localfitsoft.html.
- Local_regression wikiPageExternalLink localregression.principles.ps.
- Local_regression wikiPageID "4146592".
- Local_regression wikiPageRevisionID "606149063".
- Local_regression hasPhotoCollection Local_regression.
- Local_regression subject Category:Nonparametric_regression.
- Local_regression subject Category:Regression_analysis.
- Local_regression comment "LOESS and LOWESS (locally weighted scatterplot smoothing) are two strongly related non-parametric regression methods that combine multiple regression models in a k-nearest-neighbor-based meta-model. "LOESS" is a later generalization of LOWESS; although it is not a true initialism, it may be understood as standing for "LOcal regrESSion".LOESS and LOWESS thus build on "classical" methods, such as linear and nonlinear least squares regression.".
- Local_regression label "Local regression".
- Local_regression label "Regresión local".
- Local_regression sameAs Regresión_local.
- Local_regression sameAs m.0blm1l.
- Local_regression sameAs Q6664520.
- Local_regression sameAs Q6664520.
- Local_regression wasDerivedFrom Local_regression?oldid=606149063.
- Local_regression depiction Loess_curve.svg.
- Local_regression isPrimaryTopicOf Local_regression.
