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hbiostat.org | ||
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fharrell.com
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| | | | | Many researchers worry about violations of the proportional hazards assumption when comparing treatments in a randomized study. Besides the fact that this frequently makes them turn to a much worse approach, the harm done by violations of the proportional odds assumption usually do not prevent the proportional odds model from providing a reasonable treatment effect assessment. | |
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www.karsdorp.io
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| | | | | I'm a researcher in Computational Humanities and Cultural Evolution at Amsterdam's [Meertens Institute](https://meertens.knaw.nl/index.php/en/), affiliated with the Royal Netherlands Academy of Arts and Sciences. I study aspects of cultural change and experiment with methods to quantify cultural diversity. A significant aspect of my recent work is understanding and accounting for biases in these quantifications. I like to use computational models from fields such as Machine Learning, Cultural Evolution, and Ecology to aid these investigations. Beyond research, I have a passion for teaching computer programming, especially within the Humanities context. Together with [Mike Kestemont](http://mikekestemont.github.io/) and [Allen Riddell](https://www.ariddell.or... | |
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fharrell.com
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| | | | | Many researchers worry about violations of the proportional hazards assumption when comparing treatments in a randomized study. Besides the fact that this frequently makes them turn to a much worse approach, the harm done by violations of the proportional odds assumption usually do not prevent the proportional odds model from providing a reasonable treatment effect assessment. | |
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doomlab.github.io
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| | | Hey everybody! The last couple days I have been trying to learn LASSO regression, which stands for Least Absolute Shrinkage and Selection Operator. I have several datasets with many variables, and I thought these would be a good opportunity to learn about how to lasso, while maybe answering a few questions about words. Right, the part I forgot about is that I have repeated measures data, which always complicates things. | ||
