Autors:

CiteWeb id: 20120000009

CiteWeb score: 8623

The visual information on a scatterplot can be greatly enhanced, with little additional cost, by computing and plotting smoothed points. Robust locally weighted regression is a method for smoothing a scatterplot, (x i , y i ), i = 1, …, n, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i , y i ) is large if x i is close to x k and small if it is not. A robust fitting procedure is used that guards against deviant points distorting the smoothed points. Visual, computational, and statistical issues of robust locally weighted regression are discussed. Several examples, including data on lead intoxication, are used to illustrate the methodology.

The publication "Robust Locally Weighted Regression and Smoothing Scatterplots" is placed in the Top 10000 of the best publications in CiteWeb. Also in the category Mathematics it is included to the Top 100. Additionally, the publicaiton "Robust Locally Weighted Regression and Smoothing Scatterplots" is placed in the Top 100 among other scientific works published in 2012.
Links to full text of the publication: