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  • lmomentsets now available from SSC

    Thanks as ever to Kit Baum, a new command lmomentsets is now available from SSC.

    lmomentsets requires Stata 10.

    lmomentsets joins three other commands with similar flavour on SSC:

    cisets https://www.statalist.org/forums/for...-interval-sets 29 October 2024

    pctilesets https://www.statalist.org/forums/forum/general-stata-discussion/general/1767395-pctilesets-downloadable-from-ssc-percentile-sets 11 November 2024

    momentsets https://www.statalist.org/forums/for...ercentile-sets 11 November 2024

    lmomentsets makes this a quartet. Despite announcements and elaborations in key publications in major journals, L-moments have not broken through to front rank in statistical science.
    They are quite well known in some application areas -- especially hydrology and climatology -- but (it seems) essentially unknown or at least rarely used in many others.

    The new command is for L-moments and assembles new datasets from one or more variables, that may be used in further graphical and other analyses.

    I've tried my best to propagandise for L-moments, in talks at Stata meetings (London 1999, 2007; Boston 2005, 2007; Stockholm 2007) and in the lmoments command on SSC, first posted in 1998, last revised in 2013. https://ideas.repec.org/c/boc/bocode/s341902.html

    lmomentsets uses much of the same code as lmoments, but is independent.

    Here are some moderately recent mentions of L-moments in recenr threads

    https://www.statalist.org/forums/for...-for-normality

    https://www.statalist.org/forums/for...rginal-effects

    https://www.statalist.org/forums/for...rtosis-results

    https://www.statalist.org/forums/for...normality-test

    https://www.statalist.org/forums/for...-for-summarize

    The help for lmomentsets contains much material by way of motivation and several references. I will not repeat that here, but leave as a bottom line my personal favourite application of L-moments, the scope of the third and fourth L-moments to summarize distribution shape in ways that have many advantages over the more conventional skewness and kurtosis measures based on traditiional moments.


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