10 ms·
What are the most important statistical ideas of the past 50 years? [pdf]
- karlmcguire 6y agoStatistical Consequences of Fat Tails by Nassim Taleb.
- stblack 6y agoNot mentioned, not cited in the paper. That's shocking. Edit: the word "tail" appears nowhere in the paper, in any context. I'm beyond shocked now.
- disgruntledphd2 6y agoBecause this was well known to statisticians long before Taleb talked about it? That would be my suspicion as to why it isn't there.
- FabHK 6y agoQuite plausible. Extreme Value theory [1] appears to have been codified by the 1960s, and one of the main theorems is credited “to Fréchet (1927), Ronald Fisher and Leonard Henry Caleb Tippett (1928), Mises (1936) and Gnedenko (1943)” [2]. ETA: And the second theorem of Extreme Value Analysis is from the mid 1970s. [3] 1. https://en.wikipedia.org/wiki/Extreme_value_theory https://en.wikipedia.org/wiki/Extreme_value_theory 2. https://en.wikipedia.org/wiki/Fisher–Tippett–Gnedenko_theorem https://en.wikipedia.org/wiki/Fisher–Tippett–Gnedenko_theore... 3. https://en.wikipedia.org/wiki/Pickands–Balkema–De_Haan_theorem https://en.wikipedia.org/wiki/Pickands–Balkema–De_Haan_theor...
- bigbillheck 6y agoMy stats training was in the 90s and we absolutely covered leptokurtic things.
- selimthegrim 6y agoThe book by Leadbetter, Lindgren and Rootzen is good too if a bit dated.
- deleted 6y ago[deleted]
- spekcular 6y agoThis is subsumed in the robust estimation section.
- ojnabieoot 6y agoUnsolicited advertising for Taleb’s newest book is really not constructive or helpful, and betrays that you don’t actually know very much about statistics.
- sigstoat 6y agocalling someone's book suggestion an "advertisement" is rude, and inaccurate. taleb wouldn't pay anyone to suggest his book when he could instead just show up here and insult everyone for free.
- ojnabieoot 6y agoI didn’t mean it strictly literally. The original comment was a thoughtless namedrop of a brand new book (which means by tautology it’s irrelevant to the topic at hand) and doesn’t have a shred of reasoning behind it. So, functionally, it’s a billboard advertising Taleb’s book. I am aware that the Cult of Taleb means that people are willing to advertise his work for free.
- spekcular 6y agoI have read this book and want to leave an anti-recommendation here. It's a poorly edited mess and makes at least one blatant mathematical error. More broadly, let me leave a Taleb anti-recommendation. His entire shtick is yelling that traditional statisticians have ignored heavy-tailed random variables in their modeling and that he has special insight into the nature of tail risk (perhaps along with a few select other people, like Mandelbrot). But this is manifestly not the case. In fact, if you go through his Amazon reviews page, you can find him leaving positive reviews several years ago on all the books written by traditional statisticians that he learned about heavy-tailed randomness from!
- sfashset 6y agolink to his Amazon reviews page?
- spekcular 6y agoScroll back to the early 2010s: https://www.amazon.com/gp/profile/amzn1.account.AHMHNR4MRTDLMBOOT6Q7LX2WP5YA/ https://www.amazon.com/gp/profile/amzn1.account.AHMHNR4MRTDL... For a more detailed critique, see Robert Lund, Revenge of the White Swan, The American Statistician Vol. 61, No. 3 (Aug., 2007). Accessible through your favorite Russian website. If you want a better book on heavy-tailed randomness, I like Didier Sornette's Critical Phenomena in Natural Sciences (subtitled Chaos, Fractals, Selforganization and Disorder: Concepts and Tools).
- SkyMarshal 6y agoRevenge of the White Swan also appears available on ResearchGate: https://www.researchgate.net/publication/4741329_Revenge_of_the_White_Swan https://www.researchgate.net/publication/4741329_Revenge_of_...
- jasonwatkinspdx 6y agoTaleb is... not a good source for learning statistics. Start with Wasserman. Taleb says obvious and well known things using his own invented terminology in order to cast himself as some sort of contrarian genius. It's not that he's wrong, it's that the insights he hawks are banal. That's why his readership base are insight porn book junkies not people actually trying to learn statistical methods.
- actusual 6y ago"insight porn books" is going in my "objects you've been searching for titles for" Notion list.
- jasonwatkinspdx 6y agoYeah, I think I first heard it in relation to Malcolm Gladwell and it's just so apt at capturing everything wrong with that category of book. I mean he's a skillful writer, and it's definitely entertaining stuff. But if you flip into critical mode and do comparative research vs authoritative sources, you start seeing how vapid it is really fast.
- disgruntledphd2 6y agoWhen I read Fooled by Randomness I found it useful. Not groundbreaking work, but it drew some nice analogies between statistical distributions and human's over-certainty.
- fhsm 6y agoWould love to see that list or any other on which this choice descriptor finds itself.
- tajd 6y agoIf you do have his books then the reference lists in the back provide a good starting point for further reading.
- stevegalla 6y ago
- alexilliamson 6y agoThis feels only slightly more legitimate than recommending the 538 blog as a statistical authority.
- thepangolino 6y agoThat's a great practically oriented crash course on modern statistics!
- TameAntelope 6y agoI desperately want to get to a point where I'm able to quickly sketch out a statistical model for making a decision, but I'm struggling mightily on where to get started that doesn't include some kind of "hide the uncertainty" shell game, such as how I feel when Bayesianism gets thrown around. Submissions like this give me hope, that if I just read more, I'll get there, but nothing listed here feels like it's usable in a "napkin math" scenario. I just bought two of Gerd Gigerenzer's latest books, and I've already obtained some of David Sklansky's more introductory work on gambling, but I'm concerned my focus is either too theoretical or too narrowly on gambling (though I think there's a lot to learn in how a gambler assesses a bet, and how it applies to other situations). I guess if it were easy, we'd all be doing it...
- Arnehuang 6y agoIs that statistics? I think of statistics as "given historical data, infer future data". But it seems like what you want is to know which decision is best, which involves many more things (like estimating the impact/utility of each outcome), that seems more like economics?
- asdff 6y agoEconomics is also statistics.
- Arnehuang 6y agoI think of it like this: Suppose I want to make a decision about whether to hedge for a market crash right now. Statistics can tell me the likelihood of a crash, and how bad. But if the market crashes, and very badly, how might that affect my life? To make a good decision I would need to think of all the things that come with a market crash (job loss, savings loss). This is not statistics. I could again use statistics to say what is the chance I lose my job given a market crash (say 70%). But then I would need to estimate the impact on my life should I lose my job (Stress, etc). This is not statistics. But it should very well factor into my ability to do back of the napkin math on whether I should hedge or not.
- andyxor 6y agoCausality, i.e. Causal inference & Graphical Models, see the work by Judea Pearl, he pretty much singlehandedly pioneered the field. https://www.amazon.com/Causality-Reasoning-Inference-Judea-Pearl/dp/052189560X https://www.amazon.com/Causality-Reasoning-Inference-Judea-P... https://en.wikipedia.org/wiki/Causality#Theories https://en.wikipedia.org/wiki/Causality#Theories https://en.wikipedia.org/wiki/Graphical_model https://en.wikipedia.org/wiki/Graphical_model
- ncmncm 6y agoSingle-handedly pioneered reviving the field, but yes. Pearl is very careful to give his deceased predecessors their due credit. That their work was suppressed will always be a blot on the leading names in statistics in the past century.
- bookmarkable 6y agoBidding on NFT for this image starts at 1 ETH.
- ncmncm 6y agoThe re-discovery of causation analysis, by Pearl, after it was suppressed for many decades by the statistics mandarinate, clearly qualifies. Max Planck is quoted, "Science progresses one funeral at a time." In this case, the grand old man of statistics finally died still insisting that it could not be proved that smoking caused cancer, but not before blighting careers of those who were showing it could.
- huitzitziltzin 6y ago> after it was suppressed for many decades by the statistics mandarinate, clearly qualifies. I have heard Pearl make this claim but have never seen his evidence for it. As a counterexample: Don Rubin is a statistician who has a well-known framework for causal inference who has been at the top of the field for a very long time. Rubin has published widely and very well on causal inference. Is there good evidence for the topic actually being suppressed by anyone within the statistics profession? There is work on causal inference going back to RA Fisher. If anyone has tried to suppress it, I'm not sure they have been very effective.
- mlac 6y ago>Is there good evidence for the topic actually being suppressed by anyone within the statistics profession? Unfortunately there's not enough evidence to show causation...
- alilleybrinker 6y agoFor anyone wanting to learn causal inference (the first item in the list), I highly recommend "Causal Inference, the Mixtape" by Scott Cunningham, a professor of economics at Baylor University. Scott has been writing this book incrementally in the open for the last couple of years, and recently completed and published it, and it is a thorough introduction to numerous techniques for inferring causation in different contexts. https://www.scunning.com/mixtape.html https://www.scunning.com/mixtape.html
- cambalache 6y agohttps://mixtape.scunning.com/ https://mixtape.scunning.com/ Link to the free HTML version
- wyclif 6y agoScott Cunningham is an old internet pal of mine, before he was famous!
- beagle3 6y agoIt has to a lot to do with statistics, but is not usually classified as statistics per-se, but I think the entire field that includes Random Projections (e.g. Johanson-Lindenstrauss Lemma), Compressive Sensing and friends. Statistically speaking, it says that regardless of the dimension that data is presented in, a random linear projection to (slightly more than) the intrinsic dimension of the data captures the underlying topology.
- MrMan 6y agoThis is a huge result in signal processing but with broad applications