Look at what I found at #thriftbooks...

Probably from a library , original 1971 edition (published two years before I was born!) and one of the very good (?best) and clear books written on the topic of #linearmodels

‼ Announcement: Online Unfold.jl workshop ‼

📅 09.05.2025
💶 Free!
👉🏼 https://github.com/s-ccs/workshop_unfold_2025
❓ rERPs, mass univariate models & deconvolution!

If you are interested in combined #EEG / #EyeTracking, in natural experiments, sequential sampling models + EEG (e.g. DriftDiffusion), #VR+EEG, - this could be a useful workshop for you!

#EEG #linearmodels #statistics
#julialang

Organized with Romy Frömer (CHBH)
and the S-CCS lab (@uni_stuttgart)

📈 Models simplify complex observations by filtering out details that might not generalize to new instances, but… simplification requires assumptions.

Take #LinearModels: they assume data is fundamentally linear, dismissing deviations as mere noise.

The art lies in knowing what to keep and what to discard.

#DataScience #MachineLearning #ml #ai

"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 https://www.cmu.edu/dietrich/sds/docs/dawes/the-robust-beauty-of-improper-linear-models-in-decision-making.pdf

In today's parlance the TL;DR would read "feature selection is really important."

#DataScience #MachineLearning #LinearModels

In today's lecture on #StatisticalModeling, I explained how to define meaningful non-orthogonal hypotheses/contrasts in (generalized) #LinearModels.

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)
https://doi.org/10.1016/j.jml.2019.104038

Preprint: https://arxiv.org/abs/1807.10451