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sciruby.com | ||
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www.unofficialgoogledatascience.com
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| | | | | by NICHOLAS A. JOHNSON, ALAN ZHAO, KAI YANG, SHENG WU, FRANK O. KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction ... | |
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matbesancon.xyz
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| | | | | Learning by doing: predicting the outcome. | |
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www.fromthebottomoftheheap.net
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| | | | | [AI summary] The text discusses the use of generalized additive models (GAMs) to represent random effects as smooths, enabling the testing of random effects against a null of zero variance. It compares this approach with traditional mixed-effects models (e.g., lmer) and highlights the advantages and limitations of each. Key points include: (1) Representing random effects as smooths in GAMs allows for efficient testing of variance components and compatibility with complex distributional models. (2) While GAMs can fit such models, they are computationally slower for large datasets with many random effects due to the lack of sparse matrix optimization. (3) The AIC values for models with and without random effects are similar, suggesting that the simpler model i... | |
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gregorygundersen.com
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| | | Gregory Gundersen is a quantitative researcher in New York. | ||
