Posts Tagged ‘stats’
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November 23, 2024Books & articles on Mixed Models
November 9, 20241. Mixed Models: Theory and Applications with R. You can find their book’s website at https://www.eugened.org/mixed-models, and the PDF version of the book can be found
https://www.isical.ac.in/~arnabc/linmod/demidenko.pdf. This book is written by Prof. Eugene Demidenko, who works at Dartmouth College in the Department of Biomedical Science. I think this book emphasizes the application a lot.
2. Generalized, Linear, and Mixed Models. You can find this book from the https://onlinelibrary.wiley.com/doi/book/10.1002/0471722073; This book is written by Prof. Shayle R. Searle (a leader in the field of linear and mixed models in statistics who worked at Cornell) and Prof. Charles E. McCulloch (a professor of Biostatistics at UCSF). This book emphasizes the theoretical part of the model.
3. I also found a
https://biol609.github.io/Readings/McNeish_Kelley_PsychMethods_2019.pdf that summarizes and compares Fixed and Mixed-effect models.
McNeish, D., & Kelley, K. (2018). Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations.
Psychological Methods, 24(1), 20–35.
https://doi.org/10.1037/met0000182
Physics-Informed Neural Networks: An Application-Centric Guide | by Shuai Guo | Towards Data Science
November 2, 2024Hopfield nets and the brain. In this article we will be discussing… | by Serban Liviu | Medium
October 24, 2024Great article connecting the Hippocampus’s CA3 to the Hopfield Network https://medium.com/@serbanliviu/hopfield-nets-and-the-brain-e5880070cdba
Breaking down Neural Networks: An intuitive approach to Backpropagation | by Benedict Florance Arockiaraj | Spider R&D | Medium
October 24, 2024A review of the 25 most popular distributions in statistics using Python | by Crystal X | Sep, 2024 | Medium
September 22, 2024Probabilistic interpretation of linear regression…
September 1, 2024Annotated References on ARIMA
August 12, 2024I had to consult many references to really understand ARIMA. These are listed below.
Main Text: Forte, R. M. (2015). Mastering Predictive Analytics with R. Packt Publishing. ISBN-13: 978-1783982806, ISBN-10: 1783982802.
Suggested: Dalinina, R. (2017). Introduction to Forecasting with ARIMA
in R. https://blogs.oracle.com/ai-and-datascience/post/introduction-to-forecasting-with-arima-in-r
Nice discussion of direct and indirect effect in relation to ACF: Rajbhoj, A. (2021, December 11). ARIMA Simplified. Towards Data Science. Medium.
https://towardsdatascience.com/arima-simplified-b63315f27cbc
Fantastic intuition on MA processes: Sosna, M. (2021). A Deep Dive on ARIMA Models. https://mattsosna.com/ARIMA-deep-dive/#ma-moving-average Very good on the technical details of how exponential smoothing relates to ARIMA:
Nau, R. Introduction to ARIMA Models. Fuqua School of Business. https://people.duke.edu/~rnau/411arim.htm
Other stuff: Ariton, L. (2021, December 27). A Thorough Introduction to ARIMA Models. Analytics Vidhya – Medium.
https://medium.com/analytics-vidhya/a-thorough-introduction-to-arima-models-987a24e9ff71
Other stuff #2: Li, R. (2024, February 7). Prediction: Time Series Forecasting vs Regression. Richard Li – Medium.
https://medium.com/@rdli/prediction-time-series-forecasting-vs-regression-b4ce3159b3f2
Introduction to Forecasting with ARIMA in R
August 11, 2024https://blogs.oracle.com/ai-and-datascience/post/introduction-to-forecasting-with-arima-in-r
Dalinina, R. (2017). Introduction to Forecasting with ARIMA in R.
https://blogs.oracle.com/ai-and-datascience/post/introduction-to-forecasting-with-arima-in-r
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