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In the article "Lasso (statistics)" the main object is written all the time as LASSO. In literature, there are three ways of writing it, namely "lasso", "Lasso" and "LASSO" are used. I think it should be changed to the style "lasso" as introduced by Tibshirani (1996) to be neutral. But, all three options should be mentioned. — Preceding unsigned comment added by 87.139.244.85 ( talk) 07:13, 19 January 2016 (UTC)
I think the 1/N prefactors throughout the article shouldn't be there. They're not in the LASSO, as far as I understand. They lead to confusion. I don't want to edit the article myself, I think an expert should step in and change things. Hope this comment is ok, I've never commented on Wikipedia before. 128.218.42.15 ( talk) 02:11, 22 March 2017 (UTC)
Several different nouns are applied to LASSO throughout the article, including:
But most often it is referred to as "the lasso", which grammatically incorrect and avoids putting it in any wider scientific context.
I find all this confusing (if not inconsistent). The article lacks clarity as to where exactly LASSO stands as a concept, a class of optimisation problems, an approach, or a method; and what category its analogues fall into.
Someone with sufficient depth and breadth of subject knowledge should address this issue. AVM2019 ( talk) 19:24, 10 September 2020 (UTC)
In the section "Making λ easier to interpret with an accuracy-simplicity tradeoff" the terms "data-optimized" OLS is used, which I haven't seen in any scientific literature on the subject yet. Searching the net for this term, I only found it in the given reference by Victor Hoornweg, who introduced this section into the article himself as user Beos123. However, this reference does not seem to be peer-reviewed or reflect general scientific consensus, so I've marked the article as containing original research. I can't judge whether the content of the section is good, or correct, or helpful (maybe it is, maybe it's not), but I think it violates WPs original research principle. Especially the terminology (data-optimized OLS, hypothesized coefficients, ...) seems very idiosyncratic to me, and I think the section should be removed. — Preceding unsigned comment added by Ezander ( talk • contribs) 09:18, 2 March 2021 (UTC)
Dear Ezander, thank you for asking questions about the source of this section. It is based on the
PhD dissertation of Victor Hoornweg, peer-reviewed by the doctoral committee of Erasmus University Rotterdam. In the article, 'Good ridge estimators based on prior information' by B. F. Swindel (1976), the author explains that 'might well be chosen to reflect as well as possible the prior information or hypotheses on b'. Hence the term 'hypothesized regression parameters'. The term 'data-optimized OLS' is used for explanatory purposes. If λ equals 0, the hypotheses have no influence on the estimated coefficients, so that the estimates are only based on data-optimization, which results in the OLS solutions. This helps, I hope, to explain that λ balances between prior hypotheses and the data (see "prior lasso" below). Greetings, Victor Hoornweg (Beos123)