21–30 of 55 posts
Happy 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…
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.
Happy 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…
Suggestions for a more rigorous treatment?
and Hogg, McKean and Craig: 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.
Who 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…
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.
Can 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. :)
Probability is at the heart of the project: frequencies that summarize reoccurring data. Instead of storing a reoccurring pattern multiple times, we just store it once and record how often it has occurred.
Who 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…
Who 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…
Happy 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…
Suggestions for a more rigorous treatment?
Earlier quoted context omitted.
Suggestions for a more rigorous treatment?
Anything with measure theory, though you will regret it :)
Can 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. :)
It 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?
Excellent summary, thank you.