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Computer Age Statistical Inference: Algorithms, Evidence and Data Science
- brudgers 9y agodirect link to pdf, https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf
- gbrown 9y agoAwesome! I love the recent trend of making these books freely available.
- gaius 9y agoThis is good too: http://genomicsclass.github.io/book/ http://genomicsclass.github.io/book/ also at https://leanpub.com/dataanalysisforthelifesciences https://leanpub.com/dataanalysisforthelifesciences
- digitalzombie 9y agoEfron is the creator of bootstrap. Hastie iirc is one of the two responsible for LASSO, Ridge, and I think elasticnet.
- dcl 9y agoNot ridge, that was popularised by Hoerl and Kennard (1970). However, the method of augmenting the diagonals of the X'X matrix (ridge regression) was studied by Tikhonov in the early 40's as a method for solving ill-posed problems. I believe Hastie has done lots of work on LASSO/L_1 norm related regularization methods and algorithms though.
- neves 9y agoHow much math must one know to be able to read this book? Is this an introductory book?
- efm 9y agoIt assumes you know a fair bit. McElreath's _Statistical Rethinking_ is a better introduction: http://xcelab.net/rm/statistical-rethinking/ http://xcelab.net/rm/statistical-rethinking/
- digitalzombie 9y agoI don't think statistical rethinking is a good book for general statistic at all. It's more a good book for introduction bayesian statistic. And it's super math/stat lite. If you really want to stick with Bayesian Statistic and need a comprehendsive stat for a foundation a better book would be: Doing Bayesian Data Analysis by John K. Kruschke.
- kgwgk 9y agoThis is not an introductory book at all. From the preface: “Our intention was to maintain a technical level of discussion appropriate to Masters’-level statisticians or first-year PhD students.”
- uptownfunk 9y agoAnother amazing book from Hastie/Efron. The ISLR book was my first foray into ML and landed me my current job. Will be sure to devour this one as well!
- leesec 9y agoCan you specify which book you mean, when you say "ISLR"? Thanks!
- cjauvin 9y agoThis one: http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/ Not to be confused with this one: https://web.stanford.edu/~hastie/ElemStatLearn/ https://web.stanford.edu/~hastie/ElemStatLearn/ (a.k.a. ESL)
- NabenHarb 9y agoDo you happen to know how the two books compare to each other?
- R_haterade 9y agoElements is much more rigorous and also longer.
- dapreja 9y agoCould you tell me how exactly did you land (and what it is) your current job, and from what previous possition did you come from?
- uptownfunk 9y agoCurrently a Data Scientist for an MBB consulting firm. Came from a quant role (as a Project Manager) from an Ibank. Everything I do in my current role is pretty much straight from ISLR.
- graduation 9y agoFor those who know, How does this book differ from Foundations of Data Science by Blum/Hopcroft/Kannan ? http://www.cs.cornell.edu/jeh/book%20June%2014,%202017pdf.pdf http://www.cs.cornell.edu/jeh/book%20June%2014,%202017pdf.pd... ?
- ice109 9y agocomparing the contents the efron hastie book is something like the "gentle introduction to ___" books you see in math. as opposed to the hopcroft book which i've had on my digital shelf for a while now - it's no joke wrt mathematical maturity and depth.
- Real_S 9y agoAfter looking through the Contents of both: CASI looks very cutting edge, but also covers the origins of the use of computers for statistical methods. So this covers how computers can and have been used to do statistics. FoDS looks like a very thorough data science book. Less of a "then VS now" book and more of a collection of what you need as a data scientist. In CASI, I like the chapter on FDR, a very important topic not found in FoDS (?!). FDR is critical for correcting for multiple testing, seems essential for data science but maybe the authors consider it cutting edge and not foundational. However, the wavelet chapter in FoDS makes me happy, a very useful topic for series data. Both great books, thanks for the links!
- joshvm 9y agoHow do you approach a publisher with the free PDF model? Is it something they're generally open to? (e.g. you have a corpus of work on a website that you want to turn into a book)
- kristjansson 9y agoTangential: for anyone reading the PDF, pdfcrop --verbose --margins "20 30 20 30" --bbox "110 180 440 740" casi.pdf trims the over-large margins without clipping any content. http://manpages.ubuntu.com/manpages/precise/en/man1/pdfcrop.1.html http://manpages.ubuntu.com/manpages/precise/en/man1/pdfcrop....
- kranner 9y agoFor these parameters, the latest pdfcrop (v0.4b) dies with the following errors: Error! Bounding Box borders imply page width of zero. Error! Bounding Box borders imply page height of zero.
- petters 9y agoNow let's hope that we can start using these methods more in science instead of e.g. p-values.
- gaius 9y agoEffect size in addition to p-value, not instead of...
- Houshalter 9y agoNo there are bayesian replacements for p values as well: https://replicationindex.wordpress.com/2015/04/30/replacing-p-values-with-bayes-factors-a-miracle-cure-for-the-replicability-crisis-in-psychological-science/ https://replicationindex.wordpress.com/2015/04/30/replacing-...
- martingoodson 9y agoI can really recommend this book. It's an enjoyable read and is very pragmatic. A useful reference for practitioners.
- wenc 9y agoI've read ISL and ESL. ISL is a practical introduction to concepts, and ESL goes deeper into the algorithms. This books seems like a historical survey of how things came to be, rather than a practical guide. I believe this is not a book for practitioners, but for someone who is looking to advance the field (researchers, Ph.D. students). As someone who was in academia in 12 years, I think it is immensely valuable to have a survey like this because too often new researchers don't have a clear idea what has been tried before and why they failed or succeeded.
- awblocker 9y agoStatistician/data scientist here. This is one of my favorite texts in the area. It frames and groups methods historical rather than mathematically. I've found it both a valuable teaching tool and an interesting read on its own. Highly recommended.