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blog.stata.com | ||
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finnstats.com
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| | | | | Nonlinear Regression Analysis in R. We learned about R logistic regression and its applications, as well as MLE line estimation and NLRM. | |
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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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sciruby.com
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| | | | | Google Summer of Code 2015 is coming to an end. During this summer, I have learned too many things to list here about statistical modeling, Ruby and ... | |
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aimatters.wordpress.com
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| | | A few weeks ago, it was announced that Keras would be getting official Google support and would become part of the TensorFlow machine learning library. Keras is a collectionof high-level APIs in Python for creating and training neural networks, using either Theano or TensorFlow as the underlying engine. Given my previous posts on implementing an... | ||
