Ancestors

Written by Achim Zeileis on 2024-09-16 at 08:02

📢 PSA: Tired of #betareg in :rstats: complaining when you have 0 and/or 1 in your response variable?

🎉 Good news: betareg can now capture this at the expense of a single extra parameter! #rstats

📄 New arXiv working paper with @ikosmidis

https://www.zeileis.org/news/xbx/

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Toot

Written by Achim Zeileis on 2024-09-16 at 08:04

💡 Idea for extended-support beta mixture (XBX) distribution:

• Extend support via 4-parameter beta from (0, 1) to (-ν, 1 + ν)

• Special cases: Beta for ν = 0 and Normal for ν → ∞

• Shrink towards beta by exponential continuous mixture for ν

• Censor outside [0, 1] to obtain point masses at 0 and 1

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Descendants

Written by Achim Zeileis on 2024-09-16 at 08:05

🧭 This was a long journey of almost a decade (interrupted by a couple of kids and a pandemic).

2015: First idea and basic theory.

2017: First implementation on R-Forge, work out details.

2024: Full implementation, application, paper. 🥳

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Written by Facundo Muñoz on 2024-09-18 at 13:39

@zeileis Thanks for pointing this out!! It's comforting to see that these things take a lot of time, even to very clever people.

I get frustrated too often because I feel I should be capable of "solving" a problem or implementing a methodological idea in a few days of work.

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Written by Achim Zeileis on 2024-09-18 at 14:32

@famuvie Most of the cleverness was done by @ikosmidis almost 10 years ago. 😇

After that it was more tedious work, implementation, embellishing the application case study, and weaving everything into a coherent story for the paper. And these things just need time for thought and trying out different ideas.

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Written by Facundo Muñoz on 2024-09-18 at 13:33

@zeileis I didn't quite get the advantage of using the XBX continuous mixture model over the simpler XB truncated model, as both require estimating an extra parameter (with different roles).

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Written by Achim Zeileis on 2024-09-18 at 14:29

@famuvie We briefly discuss this in the paper. With XB you get all sorts of identifiability problems which are mitigated by shrinking it towards the beta distribution.

But if you want to play around with this, you can via betareg(..., dist = "xbeta"). But you might often encounter problems with starting values, convergence, etc.

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