Hachyderm.io

0 posts0 participants0 posts today

Benedikt Ehinger‼ Announcement: Online Unfold.jl workshop ‼📅 09.05.2025 💶 Free! 👉🏼 <a href="https://github.com/s-ccs/workshop_unfold_2025" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://github.com/s-ccs/workshop_unfold_2025</a> ❓ rERPs, mass univariate models & deconvolution!If you are interested in combined <a href="https://scholar.social/tags/EEG" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EEG</a> / <a href="https://scholar.social/tags/EyeTracking" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EyeTracking</a>, in natural experiments, sequential sampling models + EEG (e.g. DriftDiffusion), <a href="https://scholar.social/tags/VR" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#VR</a>+EEG, - this could be a useful workshop for you!<a href="https://scholar.social/tags/EEG" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EEG</a> <a href="https://scholar.social/tags/linearmodels" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#linearmodels</a> <a href="https://scholar.social/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#statistics</a> <a href="https://scholar.social/tags/julialang" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#julialang</a> Organized with Romy Frömer (CHBH) and the S-CCS lab (@uni_stuttgart)

Valeriy M., PhD, MBA, CQFThis study shows that in time series forecasting, a carefully designed **linear model can be all you need**. 🔗 Read the full paper: [arXiv:2502.03571](<a href="https://arxiv.org/pdf/2502.03571" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://arxiv.org/pdf/2502.03571</a>) <a href="https://sigmoid.social/tags/TimeSeries" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#TimeSeries</a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#MachineLearning</a> <a href="https://sigmoid.social/tags/Forecasting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Forecasting</a> <a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> <a href="https://sigmoid.social/tags/LinearModels" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LinearModels</a> <a href="https://sigmoid.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DataScience</a>

brozu ▪️📈 Models simplify complex observations by filtering out details that might not generalize to new instances, but… simplification requires assumptions. Take <a href="https://mastodon.uno/tags/LinearModels" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LinearModels</a>: they assume data is fundamentally linear, dismissing deviations as mere noise. The art lies in knowing what to keep and what to discard. <a href="https://mastodon.uno/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DataScience</a> <a href="https://mastodon.uno/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#MachineLearning</a> <a href="https://mastodon.uno/tags/ml" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ml</a> <a href="https://mastodon.uno/tags/ai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ai</a>

David Colarusso"The Robust Beauty of Improper Linear Models in Decision Making" lives rent free in my mind. I think about this paper from 1979 ALL. THE. TIME! TL;DR: experts can make robust linear models by just picking a few salient features from their experience. See <a href="https://www.cmu.edu/dietrich/sds/docs/dawes/the-robust-beauty-of-improper-linear-models-in-decision-making.pdf" rel="nofollow noopener noreferrer" target="_blank">https://www.cmu.edu/dietrich/sds/docs/dawes/the-robust-beauty-of-improper-linear-models-in-decision-making.pdf</a>In today's parlance the TL;DR would read "feature selection is really important."<a href="https://mastodon.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DataScience</a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#MachineLearning</a> <a href="https://mastodon.social/tags/LinearModels" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LinearModels</a>

Daniel HeckIn today's lecture on <a href="https://mastodon.social/tags/StatisticalModeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#StatisticalModeling</a>, I explained how to define meaningful non-orthogonal hypotheses/contrasts in (generalized) <a href="https://mastodon.social/tags/LinearModels" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LinearModels</a>.I only learned about the difference between specifying a contrast matrix vs. a hypothesis matrix in this paper:How to capitalize on a priori contrasts in linear (mixed) models (by Daniel Schad et al., 2020) <a href="https://doi.org/10.1016/j.jml.2019.104038" rel="nofollow noopener noreferrer" target="_blank">https://doi.org/10.1016/j.jml.2019.104038</a>Preprint: <a href="https://arxiv.org/abs/1807.10451" rel="nofollow noopener noreferrer" target="_blank">https://arxiv.org/abs/1807.10451</a>

Recent searches

Search options

Administered by:

Server stats:

#linearmodels