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This looks like a recent hot topic mainly for machine learning.
Basic idea: use calibration data, separate from estimation/training data, to estimate quantiles and prediction sets or intervals for …
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trying again for an overall design or design requirements for control chart
main original issue #4191 plus 2 PRs for earlier implementation
newer topics
- control charts based on beta distributio…
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Problem: XGBoost is a great library, but it currently lacks reliable modern uncertainty quantification that is rather easy to implement using conformal prediction. https://github.com/valeman/awesome-…
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Since, our clinical metrics evaluation has lot of outliers (due to difficulty in automatic evaluation of these metrics), we need to filter outliers when line fitting to obtain relationship between DIC…
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In the model page (https://github.com/unit8co/darts?tab=readme-ov-file#forecasting-models) we can see that for AutoARIMA and StatsforecastAutoCES probabilistic forecasting is set to not applicable but…
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### Describe the bug
Hello,
I built the source of the main branch and ran the tests. The quantile estimates calibration test is failing after 2 mins 33 sec and a lot of processor usage.
Than…
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In [Distributional Reinforcement Learning with Quantile Regression](https://arxiv.org/pdf/1710.10044.pdf), they propose a testing environment where wind is added to the environment to make a gridworld…
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We have issues for using Mallow's or Schweppe's weights but I don't see any functional form for it.
We need references and functions for those.
Then we could use var and freq weights in GLM, based…
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It seems like there is no possibility for weights, but I know that other software packages (in R and Stata) allow for weighted quantile regression. I think it should not be too difficult, as the "unwe…