Talk:Local regression

message: Since the mathematics behind this method of statistical analysis may not be understandable to anyone who has not done an undergraduate maths or statistics degree, is it perhaps appropriate to "dumb down" the introduction paragraphs slightly? 2A01:388:205:311:0:0:1:2 (talk) 16:58, 19 January 2018 (UTC)
thread_title: Technicality question

message: "n" is not defined (probably means number of observations), and "x" is not defined. It's not acceptable to write equations and not define the terms--very poor practice. Would the author please include them? Thanks! Chafe66 (talk) 19:47, 4 February 2016 (UTC)
replies: text: Back I come, one year later, and the variables are still not defined. Perhaps no one is watching the talk page. If anyone is feeling like they own this page, please step up. Otherwise...section should be rewritten to normal standards (defining variables that appear in equations). Chafe66 (talk) 01:23, 9 February 2017 (UTC)
replies: text: There are a lot of places where variables are not defined. This article needs work. The NIST page referenced does, however, define n, so I'll add that to the page. Geoffrey.landis (talk) 13:42, 14 April 2017 (UTC)
thread_title: Variables Not Defined

message: On the local regression page, under "Localized subsets of data" it is stated that 
"Useful values of the smoothing parameter typically lie in the range 0.25 to 0.5 for most LOESS applications."
while on the Bootstrap aggregating page, under the Example section, it is stated 
"The lines are clearly very wiggly and they overfit the data - a result of the span being too low."
Are they contradictory statements?
Astrobob.tk (talk) 21:14, 14 May 2016 (UTC)
thread_title:  Span in LOESS 

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thread_title:  External links modified 

message: The article reads as if Cleveland's loess is all there is in local regression. I think there's plenty of presentations of local regression without the "robustness" parts that loess has in there. I think it would make for a cleaner, more understandable presentation to have a more generic article on local regression (referencing any number of sources) and within that a substantial section on LOWESS/loess (which is important but not the whole area). 
Glenbarnett (talk) 23:03, 28 January 2018 (UTC)
thread_title:  LOWESS/loess is not all of local regression 

message: An editor has asked for a discussion to address the redirect Locally Weighted Learning. Please participate in the redirect discussion if you wish to do so.  Widefox; talk 15:05, 28 July 2019 (UTC)
thread_title:  Locally Weighted Learning listed at [[Wikipedia:Redirects for discussion|Redirects for discussion]] 

message: I'm not a maths graduate, but I was exploring non-parametric locally-weighted trend-lines, and I was just wondering if anyone uses weights based on the formula for the bottom-left corner of a circle of radius one centered at (1,1), ie
Weight (w) = 1 - (2|d| - d^2)^0.5, where d is the distance along the horizontal axis of the data-point from the point you want to estimate, scaled so the maximum distance is 1? Below I've included an example of its application on Australian youth unemployment data with the trend-line being based on a radius of nine data-points. It does not use regression techniques, nevertheless, it sure fits the data a lot better than the simple asymmetric (lagged) and unweighted moving averages built into Excel. 
I've also included a second version with the same data, basing the trend-line weights on Weight (w) = (1 - (2|d| - d^2)^0.5)^0.5, extending the smoothing range to 19 data-points either side, and adding interpolated points based on the average of the adjacent trendline points plus or minus an eighth of the difference between the average of the immediately adjacent trendline points and the average of the next closest trendline points (increasing it if the far points are lower than the near points, and vice versa). It produces a result very similar to Lowess smoothing.
I was hoping to learn what I'd have to do to get from the weights to a Lowess regression, but this article doesn't really specify the method in a way that can be easily followed.
The article mentions Cleveland (1979), but provides no links to the text. Would a circular-function-based weighting be consistent with Cleveland (1979)?
I notice that it would have more of a spiked-central weighting than any of the weighting functions mentioned in Kernel (statistics)
Alternatively, it would be useful if the article included a brief summary of the recommended conditions for a weighting system. MathewMunro (talk) 15:51, 12 March 2021 (UTC)
thread_title:  Weights 

message: "LOWESS (locally weighted scatterplot smoothing)" --In the abbreviation "LOWESS", where does the capital E come from?  It can't come from "estimated", because that isn't part of the expansion.  Why is it not known as LOWSS, rhyming with "dose"?  — Preceding unsigned comment added by 82.209.132.154 (talk) 08:46, 29 January 2023 (UTC)
replies: text: I'm pretty sure there's an E in it for the same reason there's an O in it. It's not a true acronym. It's "LOcally WEighted Scatterplot Smoothing." Columbo2014 (talk) 20:03, 15 June 2023 (UTC)
thread_title: LOWESS