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Dear Authors,
I found your paper interesting and had a question. For the distributional critic, why is the number of quantiles (51 as reported) not equal to the noise vector dimension (5 as report…
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(semi-random ideas, while thinking about robust or quantile regression)
objective:
find inlier `exog` observations so we can identify outliers that are possibly influential observations. If we downwe…
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Hello can you provide a Python Notebook for "How to do forecasting using Mamba?"
How to use Mamba to forecast and compare with LSTM, RNN also.
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possible target for 0.14: censored regression models, and self selection
two PRs
- #7637 Tobit with right and left censoring
- #7636 heckman selection model
censoring will be conceptually ea…
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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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Hi! I have several questions/requests regarding value learning https://github.com/deepmind/rlax/blob/master/rlax/_src/value_learning.py
1. If I want to use the `_quantile_regression_loss` without …
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Would you be able to shed some light on when to use the distribution fitting features of CatBoostLSS versus the quantile regression feature of the standard CatBoost package?
m1sta updated
3 years ago
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see
- #3221
- #8129
- #9227
**update** basic models are merged, extensions missing, e.g. essentially only biweight norm has full support
Maronna and Yohai 2017 recommend MM-estimator for p < …
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Dear Professor Paul Gustafson (@paulgstf ),
Hope you are having a great weekend.
I've encountered to some error messages for the JAGS part.
I wrote an Rmarkdown file which demonstrates the qu…
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(Just a vague idea about how this can be incorporated in a generic way.)
Kerby uses parameter classes in several model, mainly to handle various transformations.
mlefit uses parameter classes includi…