Definition:Lowess
(Redirected from Definition:Locally Weighted Scatterplot Smoothing)
Jump to navigation
Jump to search
Definition
Lowess is a technique for fitting a smooth curve to a large data set that is resistant to outliers.
It is an extension of the concept of a weighted moving average as used in time series analysis.
This article is complete as far as it goes, but it could do with expansion. In particular: details of what it is You can help $\mathsf{Pr} \infty \mathsf{fWiki}$ by adding this information. To discuss this page in more detail, feel free to use the talk page. When this work has been completed, you may remove this instance of {{Expand}} from the code.If you would welcome a second opinion as to whether your work is correct, add a call to {{Proofread}} the page. |
Also known as
Lowess is also known as loess.
However, the latter term can cause confusion with the technique which is a modification of lowess proper that does not involve the separate process for reducing the influence of outliers.
Also see
- Results about lowess can be found here.
Historical Note
The lowess technique was initially developed in $1979$ by William Swain Cleveland.
Linguistic Note
The word lowess is an acronym for locally weighted scatterplot smoothing.
Sources
- 2008: David Nelson: The Penguin Dictionary of Mathematics (4th ed.) ... (previous) ... (next): lowess