Want to learn about dynamic modeling for daily diary, experience sampling, ecological momentary assessment data? 😎

Register for our online course ‘Modeling the dynamics of intensive longitudindal data’ which starts in October 2025! 🤩

utrechtsummerschool.nl/courses/data...

Many thanks to my amazing supervisors and co-authors @leonievogelsmeier.bsky.social, Joris Mulder, and @joranjongerling.bsky.social

I’m really excited to share that our first article has been published in the Br. J. Math. Stat. Psychol. doi.org/10.1111/bmsp...

In this paper, we evaluate and compare different non-parametric approaches for modeling non-linearity in psychological intensive longitudinal data.

Thank you very much and thank you for all your invaluable input, guidance, and support throughout this project.

📌

Congrats!

Then I am looking forward to reading more about your analysis in the future. Do you think using a Bayesian model with priors on the number and location of the discontinuity points could be interesting for your data?

Did you by chance publish some more details on your data and analysis somewhere? I think it would be really interesting to see more on the robustness of this figure.

Thanks for the feedback, I finally got around to increase the thickness of the fitted curves. It does look better that way.

❗️Our next workshop will be on February 13th, 6 pm CET on Gaussian Process Regression in R and Stan by @janfailenschmid.bsky.social!
Register or sponsor a student by donating to support Ukraine!
Details: bit.ly/3wBeY4S
Please share!
#AcademicSky #EconSky #RStats

On February 13 we will have a workshop on Modeling Non-Linear Relationships: An Introduction to Gaussian Process Regression in R and Stan by @janfailenschmid.bsky.social
More info: bit.ly/4iQSzI7
Please share!
#RStats #EconSky #AcademicSky

I am very grateful to my incredible supervisors and co-authors, @leonievogelsmeier.bsky.social , Joris Mulder, and
@joranjongerling.bsky.social, for their invaluable contributions, guidance, and endless support throughout this project.

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In this project, we review three non-parametric and non-linear regression techniques - local polynomial regression, Gaussian processes, and generalized additive models - within the context of intensive longitudinal data and compare how well these methods can recover psychological processes.

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New Preprint!

I’m excited to share our preprint titled: "Modeling Non-Linear Psychological Processes: Reviewing and Evaluating Non-Parametric Approaches and Their Applicability to Intensive Longitudinal Data."

Check out the full preprint here: osf.io/preprints/ps...

Any feedback is welcome!

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