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www.rdatagen.net | ||
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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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austinrochford.com
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| | | | | Splines are a powerful tool when modeling nonlinear relationships. This post shows how to include splines in a Bayesian model in Python using pymc3. In addition, we will show how to use a second splin | |
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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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www.law.cornell.edu
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| | | [AI summary] The text discusses the legal and historical context surrounding campaign finance regulations, particularly focusing on the regulation of corporate and union political speech. It highlights key Supreme Court cases such as Buckley v. Valeo, Citizens United v. FEC, and McConnell v. FEC, which have shaped the understanding of corporate expenditures in elections. The text emphasizes the distinction between direct contributions and independent expenditures, the role of PACs, and the rationale behind restrictions on corporate spending to prevent corruption and the appearance thereof. It also references the Federal Election Campaign Act (FECA) and the Bipartisan Campaign Reform Act (BCRA), noting how they have been upheld by the Court as legitimate exer... | ||
