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Of course, the solution is to simply check gradcafe less. But we all know the likelihood of that.

I'm guessing that it is, however, extremely relevant for second-third year students in the midst of the transition. Though, as you said, you will probably have more time to adjust to a research schedule.

In academia, I'm pretty sure Friday counts as early. Late in the week means over the weekend through next Tuesday.

For LaTeX, this post gives some of the more popular introductory materials. I believe that I used The Not So Short Introduction to LaTeX when I first learned. But once you get the basics down, the best way to learn is simply to use it regularly. If you're going to be studying materials either way, take the time and typeset solutions to the problems that you do. If you come across something you don't know how to do (which you probably will a lot), spend some time on Google, the wikibook, and tex.stackexchange until you know how to do it. It's been a long time since I've learned R, so unfortunately I don't really remember what I used. The official introduction seems decent and comprehensive, if a bit dry. Again, I suggest learning by doing once you have the basics down, if you have any data analysis side-projects or hobbies then you should try to use R with them.

It's shocking how calm these forums are. I get anxiety just reading the thread from last year, but there's barely any posts here at all. What else is coming out soon? Washington was right on time, and if that Madison is real then they are too. Maybe Harvard this week and Michigan/CMU next week?

I think that it's a very good book,but I'm not sure you really need to brush up on it before graduate school. That's where you'll learn it. To me it seems more important to brush up on fundamentals than to repeat material you will be learning your first year. Linear algebra is extremely fundamental, often needs a brush up (most people seem to take it fairly early in undergrad), and your classes will probably assume that you know it. I think measure theory is helpful because it's something that most undergrads haven't seen before. Knowledge of it is required for graduate probability, but I'm guessing that they will go over it more quickly than might be optimal in order to get to the probability stuff.

From what I've heard, probability is generally the most difficult first-year class. I think that working on the first several chapters of a measure-theoretic probability book (Billingsley, Resnick) or the measure theory portions of an analysis book would be a more efficient use of time than studying all of Rudin. I have also heard that brushing up on basic linear algebra is very beneficial. But this is all second-hand.

Applying to PhD programs in the winter and just wanted to get a feel for my position. I am a little concerned that I have too much of an economics background and not enough stat/math in my resume. This also affects the recommendation letters I have access to, as my best ones are all in economics rather than stat/math. I'm not sure whether/how I should sell that angle. Asian Male 3.86 GPA, top 10 private university BA in Economics, Statistics GRE: 167V, 170Q. Not taking the math GRE. Classes: Analysis I-III (A, A-, A-), Probability (A), Stochastic Processes (A), Stat Theory I-II (A, B ), Linear Algebra (A), Time Series (A), Applied Stat (graduate, A-) many advanced econ classes (A/A- mostly) Experience: Significant economics research, including RAing in college and working for a couple years as an RA afterwards. Currently in the submission process as a coauthor on an econ paper that will hopefully be published in a solid journal, though, again, I'm not sure how to sell this. LoR: Great rec from a relatively unknown economics academic, decent rec from a well-known stats professor, and either an okay rec from a very well known economics professor or a very good rec from a less-known one. My interests are fairly flexible, but currently I'm most interested in statistical learning and computational statistics. I have the most fun when I get to spend a lot of time coding, so more applied work seems to be a better fit. Not particularly interest in biostat, but I could be convinced. I'm looking at a lot of top schools, though I'm not sure how much of a chance I have at many of them. Berkely, Washington, UChicago, CMU, UW-Madison, UMich, Columbia. I'm also looking for more schools that might not have top rankings but are good fits if anyone has suggestions.