DanielWarlock

I completely agree with doc's assessment. In fact, I can observe this trend at Harvard. The inference class this year is taught from a range of relatively modern topics instead of unnecessarily rigorous proofs on consistency and normality of MLE/UMVUE/NP tests and stuff like that. The measure theoretic has been downplayed a lot at Harvard as well because it is "almost completely useless". That said, the classic asymptotic techniques are still very useful. When you write some research paper, it is expected that you will give some bounding statements with *NO* exceptions and the toolkit/intuitions for doing that is pretty standard from the classics.

I observed this even in the case for the first-year Masters-level Mathematical Statistics sequence at my PhD program. The first semester, based on chapters 1-5 of Casella & Berger, is more-or-less the same, but the second semester now deviates from Casella & Berger quite a bit. They used to spend a ton of time on things like UMVUE, Neyman-Pearson, and Karlin-Rudin, but now, they either skip it or abridge it considerably, and instead, focus on topics like the EM algorithm, lasso and ridge regression, etc. By now, things like EM algorithm and lasso are not that "new," but they're certainly not relatively archaic like UMVUE or UMP tests, and they will probably be standard tools used for awhile.
I think it's a good thing. But then again, when I started to do research, I was basically learning everything on my own (I could go to my advisor for help and questions). So I can't say that most of the classes were really directly useful for research, but it didn't end up mattering in the end anyway.

I completely agree with doc's assessment. In fact, I can observe this trend at Harvard. The inference class this year is taught from a range of relatively modern topics instead of unnecessarily rigorous proofs on consistency and normality of MLE/UMVUE/NP tests and stuff like that. The measure theoretic has been downplayed a lot at Harvard as well because it is "almost completely useless". That said, the classic asymptotic techniques are still very useful. When you write some research paper, it is expected that you will give some bounding statements with *NO* exceptions and the toolkit/intuitions for doing that is pretty standard from the classics.

I'm not exactly sure about biostatistics jobs. But in finance, school names carry great weight for master/undergrad level jobs. This is called "target schools". If you are not from "target schools", your resume is placed into trash unless you have some connections in the firm. Even though people laugh at Columbia's master program as a "cash cow" but when I interviewed at banks in NYC, there are always severals students come from there. Some of those guys barely speak English and I suspect they are very good. But they are on equal footing with someone from Stanford, for example, when they got on-site interviews by Columbia name. On the contrary, I rarely see anyone from unknown state schools. It requires a great amount of networking to even get an interview if you go to Pittsburgh, which Columbia name will automatically grant you.

I'm not exactly sure about biostatistics jobs. But in finance, school names carry great weight for master/undergrad level jobs. This is called "target schools". If you are not from "target schools", your resume is placed into trash unless you have some connections in the firm. Even though people laugh at Columbia's master program as a "cash cow" but when I interviewed at banks in NYC, there are always severals students come from there. Some of those guys barely speak English and I suspect they are very good. But they are on equal footing with someone from Stanford, for example, when they got on-site interviews by Columbia name. On the contrary, I rarely see anyone from unknown state schools. It requires a great amount of networking to even get an interview if you go to Pittsburgh, which Columbia name will automatically grant you.

I'm not exactly sure about biostatistics jobs. But in finance, school names carry great weight for master/undergrad level jobs. This is called "target schools". If you are not from "target schools", your resume is placed into trash unless you have some connections in the firm. Even though people laugh at Columbia's master program as a "cash cow" but when I interviewed at banks in NYC, there are always severals students come from there. Some of those guys barely speak English and I suspect they are very good. But they are on equal footing with someone from Stanford, for example, when they got on-site interviews by Columbia name. On the contrary, I rarely see anyone from unknown state schools. It requires a great amount of networking to even get an interview if you go to Pittsburgh, which Columbia name will automatically grant you.

I don't know anything about UW. But Chicago does take some of its master students towards its PhD program. The rumour is that Chicago is insanely tough. It could potentially be a good thing if you are young and have no family obligation--you just go there and fight for your life, so to speak. But if you are anything like me, you will think twice before committing to Chicago.

I feel that there has been too much aggression and negativity. Technically, you are getting paid to learn stuff, which seems to me a pretty good deal. I used to work. At that time, I needed to do research and read paper after 7pm--I just wanted to sleep and I felt terrible. For my masters, I essentially pay tens of thousands to take same classes with PhD and do research with less support. What are we even complaining about here? I'd still do PhD if I were to go back to my old job after that.

I can't comment on your chance of getting into stats phd program because I'm also applying this year and have no idea. As to other questions, I personally think a C- in Mathematical Statistics is going to hurt at least a little because one class you will need to take in any graduate stats program is a graduate version of this. A C- is like a passing grade close to almost D so some people may question it a little bit. But this is not a death sentence because your performance on other math, stats classes are very strong, and your research is very strong too. I think you should add a note to explain why this class didn't go as well. That said, you must increase your quant GRE score for like ~10 points which seem to me is your other weakness. The practice test score is very good. Replicate that in a real test and you are good to go.

Abstract algebra seems to be completely useless for most statistics, unless you intend to work on some highly specialized areas of probability such as random walk on groups etc. If you are applying to top 10 pure math programs, I'd say you may be in trouble because abstract algebra is very essential for pure math and a B+ may put you at a disadvantage at top places. But for statistics I really don't see how it is a factor. That aside, you got B+ which is almost an A! It is not even that bad if at all. Many people would consider B+ to be an "honour grade". I would only worry about that if I had an otherwise perfect profile. But again, when your profile is that perfect on other aspects, no one will care. I strongly discourage you from including a note about grade deflation etc. because that may come off as pretentious considering that (i) there is no official, scientific quantifier as to what school has deflation; (ii) B+ is a very good score on a hard topic. It's like explaining I got 169 instead of 170 on GRE because there is a very unfair question. Makes no sense.