Michael Clark @statsdatasci

Statistical Philosopher, Brute Empiricist m-clark.github.io Ann Arbor Joined September 2016

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OneSix @Onesixsolutions

a year ago

From classic techniques to cutting-edge machine learning, data science models help uncover patterns and power smarter predictions. Check out our latest blog post for key insights from Michael Clark's book "Models Demystified": bit.ly/4hIwnic

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Michael Clark @statsdatasci

2 years ago

@IsabellaGhement @CRCPress great, thanks @IsabellaGhement !

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Michael Clark @statsdatasci

2 years ago

Hey folks! Been a while since I posted about it, but our book on practical data science modeling has come a long way since then. Would love to hear some thoughts on GitHub or here. Hopefully we'll get it done soon and out on @CRCPress in the near future! m-clark.github.io/book-of-models/

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Michael Clark @statsdatasci

2 years ago

@IsabellaGhement @CRCPress Hi @IsabellaGhement ! A quick note to say thanks again for your thoughts. Like last time I made Github issue with these suggestions so that we'll be sure to keep in mind. I've already incorporated a lot of it too! Thanks!

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Michael Clark @statsdatasci

2 years ago

@IsabellaGhement @CRCPress Thanks for your thoughts @IsabellaGhement . You are right the language is too loose/vague in several spots here (and our footnote is not enough). I've already gone ahead and made several of these and related changes. I'll also revisit a couple other spots also. Thanks again!

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Michael Clark @statsdatasci

2 years ago

Link to the repo: github.com/m-clark/book-o…

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Michael Clark @statsdatasci

2 years ago

@JKubale @stephenjwild @StaffanBetner @camjpatrick I posted on fractional and several related options with comparisons here: m-clark.github.io/posts/2019-08-… The fractional logit result can be done as a binomial glm with robust SE (among other ways). I like beta reg if applicable, but that's just me.

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Michael Clark @statsdatasci

2 years ago

@IsabellaGhement We actually had a whole section devoted to odds vs. log odds vs. probability interpretation that was taken out to shorten things up, but maybe that was hasty!

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Michael Clark @statsdatasci

2 years ago

@IsabellaGhement @IsabellaGhement Thanks much for your feedback, very helpful! We struggled with how best to present notation quite a bit, so will revisit that. Also the language gets a little too loose at times. @ChelseaParlett I'll make a note of the symmetry too, thanks!

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Michael Clark @statsdatasci

2 years ago

@atomasevic Borrow away, and yes those will be stacked.

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Michael Clark @statsdatasci

2 years ago

I've been putting together a book on modeling that I hope will appeal to a wide range of audiences, with examples in Python/R. You can check out the in-progress work at: m-clark.github.io/book-of-models/. Hope you find it useful, and feedback is appreciated as we continue to work on it!

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Michael Clark @statsdatasci

2 years ago

@arthurturrell Looks great! Will definitely add to next update!

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Michael Clark @statsdatasci

2 years ago

@RandVegan I think probably because that's what we use more often, and that when we first started out we actually were going to do things mostly 'by hand' as in my doc m-clark.github.io/models-by-exam…. We also were interested in keeping things as similar as possible between the R and Python code.

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Michael Clark @statsdatasci

2 years ago

@crcgrubbsd Yes, and very much looking forward to it!

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David Grubbs @crcgrubbsd

2 years ago

I hope @statsdatasci doesn't mind me announcing that this book will be published by Chapman and Hall/CRC likely in 2024.

Michael Clark @statsdatasci

2 years ago

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Michael Clark @statsdatasci

2 years ago

@IsabellaGhement @crcgrubbsd @IsabellaGhement here is fine but as a GitHub issue or even just email works too!

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Michael Clark @statsdatasci

3 years ago

There are good tools in #rstats (e.g. Robyn) and #python (lightweightmmm), but as noted in the article, you often will just have to roll your own (e.g. via @mcmc_stan or #numpyro).

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Michael Clark @statsdatasci

3 years ago

Been a while, but here's my new post on marketing/media mix models at the @stronganalytics blog. Those used to time series and mixed modeling might find this an interesting application. strong.io/blog/marketing…