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All of Statistics, by Larry Wassserman (2013) [pdf]
- Zuider 10y agoSadly, no link to free eBook, which is not surprising because it seems that the book is still in print, having been released as recently as 2004, and updated in 2005 and 2013. This post links to the website supporting the book and provides links to errata, code and data. The links on the page to Springer and Amazon are broken: Here are valid links: http://www.springer.com/de/book/9780387402727 http://www.springer.com/de/book/9780387402727 http://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/1441923225 http://www.amazon.com/All-Statistics-Statistical-Inference-S... Here is the Google Books link: https://books.google.ie/books?id=th3fbFI1DaMC&printsec=frontcover#v=onepage&q&f=false https://books.google.ie/books?id=th3fbFI1DaMC&printsec=front...
- rjeli 10y agoNot sure about HN's policy on posting links to pirated material, but as a Freedom of Information supporter, I will note that the book can be found at http://gen.lib.rus.ec http://gen.lib.rus.ec.
- ikeboy 10y agohttps://news.ycombinator.com/newsfaq.html https://news.ycombinator.com/newsfaq.html >In comments, it's ok to ask how to read an article and to help other users do so. Depends on just how broad article is.
- dmix 10y agoDirect link [pdf] [9mb] http://libgen.io/get/B9E6052395BA047BD154EA45130026FC/Larry%20Wasserman-All%20of%20Statistics%20-%20A%20Concise%20Course%20in%20Statistical%20Inference-Springer%20%282004%29.pdf http://libgen.io/get/B9E6052395BA047BD154EA45130026FC/Larry%...
- MichailP 10y agoCan someone say in few sentences what Statistics is all about? I can't shake off the feeling that it is just glorified curve fitting. Edit: Please stop the down votes, just an electrical engineer here, with one basic course in Probability and Stat. :)
- jsprogrammer 10y agoStatistics is applied Probability Theory. Statistics tries to find and characterize the probability distribution of 'Random Variables' through series of observations. Basically, you count things and compare that to how many things you think you should have counted given your assumptions.
- johnloeber 10y agoInferring meaning from data.
- DavidSJ 10y agoIt is just glorified curve fitting. Glorified curve fitting is a very rich field. Another way of thinking about it (described in Wasserman's book) is that statistics is the inverse problem of probability. Probability theory asks: given a process, what does its data look like? Statistics asks: given data, what process might have generated it?
- glial 10y ago> Probability theory asks: given a process, what does its data look like? Statistics asks: given data, what process might have generated it? Excellent summary, thank you.
- yoplait_ 10y agoestimating uncertainty. The curve you fit represents what you have in your data. The question is what is out there, in your target population.
- mjw 10y agoPretty much any kind of mathematical modelling that involves uncertainty, really. Making inferences and predictions from data, in the presence of uncertainty. Analysis of the properties of procedures for doing the above. If you want examples that avoid the feel of just "curve fitting" (assume you mean something like "inferring parameters given noisy observations of them") -- maybe look at models involving latent variables. Bayesian statistics has quite a few interesting examples.
- ilzmastr 10y agoHappy to see a book like this trending on hn, especially with a sentence like: "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid." in it's preface. I definitely agree, since I wasted a lot of time doing fruitless surgeries before I went and learned about band aids in depth. From my look at Part 1, it has some great coverage of the basics, all of which are important. Some of the fundamentals that I see left out are rightly left out since they require experience in real analysis to appreciate, and maybe aren't very actionable. There's few proofs, but, since the goal is a quick understanding, I can also appreciate this. It looks to me like a great intro of statistics for CS people, as the author says.
- mathgeek 10y ago> since I wasted a lot of time doing fruitless surgeries before I went and learned about band aids in depth I'm going to just assume you're being literal here. It's really brightened up my day to imagine the moment such a person discovered you could close up wounds after surgery.
- trentmb 10y agoSuggestions for a more rigorous treatment?
- tezka 10y agoMy favorites are Casella and Berger: https://books.google.com/books/about/Statistical_Inference.html?id=0x_vAAAAMAAJ https://books.google.com/books/about/Statistical_Inference.h... and Hogg, McKean and Craig: https://www.pearsonhighered.com/program/Hogg-Introduction-to-Mathematical-Statistics-7th-Edition/PGM49624.html https://www.pearsonhighered.com/program/Hogg-Introduction-to... They are both good books. The first one is more rigorous, but the second one covers more breadth and I think has better exercises.
- ilzmastr 10y agoAnything with measure theory, though you will regret it :)
- marmaduke 10y agoThis is one of the few hard cover books I have found worth its price. The statistical principles are succinctly explained such that they can be quickly implemented.
- ndr 10y agoThere's a typo in the title, it's Wasserman, 2 's' only. Can someone fix it?
- gaur 10y agoI think it's just part of the neue Rechtschreibung.
- zacharyfmarion 10y agoOne of my favorite books! Shows you how connected statistical inference and machine learning really are.
- gaur 10y agoWho is this book supposed to be for? Given the heavy emphasis on formalism (theorem, proof, theorem, proof, theorem, proof), and the lack of a single example that actually computes a number, I hazard a guess that this book is not for people who actually want to apply statistics to real problems. A while back I had to teach myself Fisher matrices and the Cramér–Rao bound to solve a problem I was working on. I quickly found that 90% of statistics textbooks and lecture notes on this subject are completely useless for people like me who want to arrive at a number, not some abstract expression involving angle brackets or measures or E[...] or whatever. The Wikipedia article on Fisher information [0] is one such example of a resource that is full of useless formal crap that crowds out an explanation for real people about how to use this statistical tool. This book appears to be of the same ilk. (Also, this book apparently does not discuss the Cramér–Rao bound. Ironic given the book's title.) If anyone is curious, the single best explanation of the Fisher matrix and the Cramér–Rao bound that I have found is tucked away in an appendix of the Report of the Dark Energy Task Force [1]. In one page they manage to concisely and clearly explain where the Fisher matrix comes from, how to compute it, and how to apply the Cramér–Rao bound. [0] https://en.wikipedia.org/wiki/Fisher_information https://en.wikipedia.org/wiki/Fisher_information [1] http://arxiv.org/abs/astro-ph/0609591 http://arxiv.org/abs/astro-ph/0609591
- hudibras 10y ago>Who is this book supposed to be for? I don't have the book here at work so I can't quote the book's introduction, but in some sense the title is meant to be literal. It's an attempt to cram an entire 4-year undergraduate statistics program into a single book, and in my opinion it's mostly successful. This book is is my go-to reference for those "Ahhhh, I remember hearing about [insert statistical test here] back in college, what was it again?" moments.
- nikkev 10y agoWhen I was taking a class in Statistical Inference, we used a combination of Statistical Inference (Casella and Berger), Introduction to Mathematical Statistics (Hogg and Craig) and Probability and Statistics (Degroot and Schervish). If you're still interested in learning about Fisher Information and the Cramer-Rao Lower Bound, you can refer to pages 514 - 521 of Probability and Statistics 8th Ed. It has a number of proofs which you can skip if you're not interested but it also provides a number of examples using different distributions to calculate both the Fisher Information and the Cramer-Rao Lower Bound.
- krosaen 10y agoThis is a major piece of my machine learning self study curriculum: http://karlrosaen.com/ml/ http://karlrosaen.com/ml/ Some links to problem set solutions there
- pddpro 10y agoHow does this compare to, say "Introduction to Statistical Learning" and "Elements of Statistical Learning" by Trevor et al? As I understand, the former is also supposed to be a concise introduction to statistical concepts while the latter offers a more rigorous treatment. Where does this book fall in between?
- kgwgk 10y agoThis book is not between ISL and ESL. All of Statistics is an introductory course (and has a much wider scope, including advanced topics) while even the watered-down ISL assumes that the reader knows already a bit of statistics.
- sandGorgon 10y agoCan someone compare this with Hastie and Tibshirani (https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...) ? I wonder which one is more practical
- huac 10y agoThe Hastie/Tibshirani book (http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/) is very practical.
- stdbrouw 10y agoWhile All of Statistics is wonderful in its genre, it really isn't a good place to start to learn statistics. Firstly because it focuses very heavily on the theory and contains very little on practical modeling. Secondly because the theory isn't even necessarily going to be very enlightening: frequentist statistics is a mathematical tour de force, using every possible hack you can think of to be able to draw statistical conclusions from nothing more than a few pen and paper calculations, but as a result frequentist theory won't actually give you any sort of deeper insight into the core theoretical foundations of probability and statistics. If you're new to statistics, try Allen Downey's http://greenteapress.com/thinkstats2/index.html http://greenteapress.com/thinkstats2/index.html or Brian Blais' http://web.bryant.edu/~bblais/statistical-inference-for-everyone-sie.html http://web.bryant.edu/~bblais/statistical-inference-for-ever.... Both are free. Then, go in depth on regression. Not just feeding in the numbers and getting back a fitted model, but actually knowing how everything works, what the common issues are, how to interpret the estimates and so on. Once you've got that down, read Regression Modeling Strategies by Harrell to go really in depth. Or if you're really just interested in prediction, Hastie and Tibshirani is wonderful of course.
- mathheaven 10y agoBrian Blais's free book doesn't contain any reference to the Poisson Distribution. For ML Hastie and Tibshirani ISLR is very good but is more for applications of machine learning: classification, regression and prediction.
- stdbrouw 10y agoThe Poisson distribution is just a limiting case of the binomial distribution and not needed to explain any of the concepts that drive statistical inference, so its absence from an introductory text is hardly something to fuss about.
- throwaway6497 10y agoPrice of this book is steep :( $90+ for new and $70+ for used.