ENE1

If you are not interested in academia, then I don't see any reason not to graduate in four years if you can. The main advantages of taking a fifth (or sixth) year are:
- more time to find a job
- more time to get more publications on your CV. 
If you aren't interested in academia, then the second point is really moot. However, for those on this forum potentially interested in academic positions: if you have a good postdoc lined up in your fourth year, then I would still recommend finishing up more quickly, even if you don't have as many publications. The main consideration for taking an extra year is whether or not you will be much more competitive on the job market with that extra year. For example, I completed my PhD in four years, and by the time I graduated, I had only one publication and two papers submitted. However, I knew that if I stayed at my program a fifth year, I wouldn't have been able to make my CV significantly stronger, so I just graduated and went immediately to the postdoc.
Meanwhile, one of my classmates could have also graduated in four years just like me, but he stayed for the fifth year so he could get an Annals of Statistics paper accepted and on his CV. This ultimately put him in a much stronger position in the academic job market and he was able to get a TT job at a very good department. So in my classmate's case, it was definitely advantageous to take the fifth year to ensure his CV was stronger.

Were any of the people here in grad school during the 2008 crash? I'd heard anecdotal stories of students being asked to self-fund, and am curious is there truth to that. More relevantly, if those concerns are applicable amid the crisis now.

Emailed UNC STOR about my application status and was told I was high on the waitlist. Hopefully I'll get a little lucky  Emailed UC Davis as well - they're still in the process of making decisions. 

Since the application season is nearing the end for PhD applicants, are people interested in creating a results thread? I created last year's thread, and if there's enough interest I can go ahead and create this year's.

Hi iwantadog,
I preface this by saying that this is my understanding gleaned mostly by reading this forum. You should read some of the other application evaluations, since it will answer your questions. Stat PhD Now PostDoc could have essentially copy+pasted one of his replies to another application evaluation; he's basically answered this before.
While stat PhD programs might *require* minimal maths requirements, they value a strong maths background above (maybe) anything else. They are looking for a strong track record showing that you can handle the theoretical requirements of the PhD. Of course, in the absence of applicants with this background, they may admit someone with the bare minimum maths requirements. But a lack of more-than-suitable applicants is rare for good schools.
Here is where your school's reputation comes in: the admission committee likely don't have the personal experience to accurately assess whether your maths courses were rigorous or not. Instead, they use the reputation of your institution is an okay proxy.
The general opinion in this forum is that most research done by applicants before their PhD bear little similarity to the research required by a stats PhD. So schools generally do not weighted this research very heavily.  So your research will help your admission chances, but usually not by much.

Not as knowledgeable as some of the other people on this subforum regarding the academics, but I can say that you absolutely need roommates if you want to live in Berkeley off that stipend (a single 1br runs $1850+ per month if you're lucky). Parking is also a nightmare if you have a car, but I wouldn't recommend it anyway in that area because the public transit is probably good enough for grad student needs. Honestly that stipend seems pretty ridiculous to me, do most Berkeley students get a fellowship or something along those lines? Have they stated that that will be your set stipend for the 4-5 years you'll be there? Because as is living off of that wage for 4 years does not seem doable without loans like @bayessays mentioned, but if they will readjust after you formally enter the PhD program that would be much more palatable.
 
Edit: I will say though that big city prices at least come with big city perks. Ann Arbor is nice but it is very clearly a college town. There are also other things to consider, especially weather. Winters are not as brutal in Ann Arbor compared to somewhere like Minnesota, but it is definitely a far cry from either of the UC schools.

UCLA will be a similar cost of living to Berkeley.  At Michigan, $24k is the 9 month stipend but you'll probably have 12 months of funding right?  Michigan still probably is best for your budget even at $24k, because Ann Arbor is cheaper than LA, but it will be by far the best stipend if you have 12 months.
What is your goal at the end of this? A lot of UCLA's well-known profs are genetics people just like Michigan. If you want to go into industry afterwards, I'd probably go enjoy the weather in LA or enjoy the extra money in Ann Arbor.  If you want to be a professor somewhere, and don't want to work in genetics, it might be worth it to go to Berkeley and work with someone like van der Laan. You will have to take out huge loans to live in Berkeley on $17k a year though.  Not counting taxes, $17k will barely rent you a bedroom there.

What are the job placements like for the schools you mentioned? For industry, it probably makes no difference. For academia, having to take these courses may be helpful in that they allow you to sharpen your proof skills, and you pick up on certain techniques from them that you can use repeatedly in your research (splitting the expectation ftw). But if you read enough papers carefully, you can probably also pick up on "standard" proof techniques.
For academic hiring at research universities, it's most important that your *research* is prolific and at least some of it is cutting-edge (i.e. getting published in the top journals or top machine learning conferences), not the content or grades of your coursework.  
Anyway, my two cents: Lehman and Casella is a very classical text but a lot of the material in it may not be very relevant to most modern statistics research (for example, L&C gives a *very* rigorous treatment of UMP tests, admissible estimators/tests, etc., which isn't a popular research topic now). I guess it's nice in that L&C has a lot of material on things like James-Stein estimation that was one of the earliest shrinkage methods (before lasso and all the sparse regression methods). But is it really necessary to know the risk/minimaxity properties of these kinds of estimators in great detail? I'm not sure.
As for probability theory, I definitely think it's good to be able to understand notation for the Lebesgue integral and know basic inequalities (e.g. union bound), but if you're a statistician and not a probabilitist, you may be able to get away with only the basics. I believe that at UC Berkeley, PhD students in Statistics do not even need to take measure-theoretic probability (they can instead take only the Applied Statistics and Theoretical Statistics sequence), and their PhD graduates seem to get along just fine.

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.

@shuggie I'd try to figure out availability of advisors (is there competition to work with certain professors?). Figure out any questions about funding you might still have, like whether you'll be funded in summers and what type of travel support there is.
I'd also try to get a gauge of the sense of community in the department and whether people seem happy.  Keep in mind they'll likely be showing you the happiest students on your visit and not the disgruntled ones.  Your first two years will basically be coursework.  Do people work together and seem to be friends with their fellow students?  Are people stressed about qualifying exams all the time? 
I'd look at coursework/quals in general a little bit, not so much for their content matter, but for stress/time purposes.  Some departments have 2 years of qualifying exams and 3 years of coursework, some places have 1 year of courses and no quals - big difference. Ask about qualifying exam passing rates.  If you are confident in your math abilities , it shouldn't be a huge issue, but some departments have pretty theoretical qualifying exams that weed out quite a few (usually domestic) students who are weaker at math. 
I would also get a good look at the facilities.  You'll be spending a lot of time in the department's buildings, and it can get depressing if you go somewhere without windows for 10 hours a day.

I got an acceptance from CMU earlier today! I thought I was out of the running after the wave of acceptances a little while ago. So there still is some hope for people still waiting?

+1 to this. Also, if you look at UC Berkeley's Statistics PhD requirements (a very top program, if I do say so myself), it seems like students there get a choice of taking two out of three sequences: theoretical statistics, applied statistics, and probability theory. So it seems as though one could actually go through UCB's Statistics PhD program without having learned even measure-theoretic probability theory (which, while good to know, probably isn't super-relevant to everyone's research --if you're not a probabilitist, you can probably learn enough of it to get by without having taken a whole course on it). And UCB also has no written qualifying exams. 
Doing original research truly is the primary focus of the PhD. For your PhD research, you are mainly teaching yourself the things needed for your research. One cannot reasonably expect to learn *everything* there is to know through classes anyway. I kept a lot of my notes from my classes but I barely look at them, because I can just Google what I need to know if it comes up (e.g. what the expectation of a quadratic form is, various norm inequalities, etc.). Plus, I will say that although my research focuses on high-dimensional Bayesian statistics, it was easy enough for me to pick up on the analogous frequentist methods/theory (like LASSO, elastic net, etc.) once I had enough experience reading and understanding academic papers. I'm sure that will be similarly the case for you.

@ENE1 I suppose it depends on your goals, but I wouldn't be too concerned about Duke's courses not giving you a solid foundation (although obviously you should probably not go to Duke if you aren't ok doing bayesian stuff).
UW has some pretty heavy course requirements and I assume you're comparing it to that, but looking at the syllabi I don't think you'll come out with a much different knowledge base.  At both schools you'll take measure theoretic probability and have the option to take stochastic processes.  Both schools will obviously have you master Casella/Berger level theory material and take basic linear models classes.  Any stat theory class beyond the Casella/Berger level is essentially a special topics course.  A lot of programs will teach you basically more theoretical stuff about sufficency, exponential families, etc, while some will have their higher-level theory classes be about bootstrap stuff, while Duke will focus mostly on Bayesian theory. Duke seems to not have an entire class devoted to large sample theory, likely because Bayesian statistics relies on computation more. Insofar as there is a "core knowledge base", all the programs you've been accepted to will provide it.
I've had the same line of thinking in the past regarding wanting rigorous coursework, but I don't think that should be a major contributor to your decision.  You're going to forget 90% of your coursework in a year if you're not using it in your research.  For instance, the poster above mentions that Duke doesn't teach much about GLMs.  As someone who went to a program that had multiple semester-long courses on them, that's crazy to me.  But 5 years later,  I don't remember much about the technical details of GLMs beyond what I could read in 5 minutes of googling.
Since all the programs will give you a solid base,  I think flexibility to take courses you want and to do research you enjoy are way more important.  The syllabi for Washington's classes are all up online, so I don't think going there based on coursework you can access for free is a good decision.

I would say that the courses are rigorous, but that there are very few requirements. Until the last year or so, technically there were no class requirements (although most students still took a majority of them). The qualifying exam after the first year is based on STA 711, STA 721, STA 611, STA 831, and STA 732.
 
The courses tend to be decently rigorous. STA 711, STA 601, and STA 721 are taken in the Fall. STA 732, STA 831, and a case studies course are taken in the spring.
STA 711 is a normal 1 semester measure-theoretic probability class (using A Probability Path). Here is the most recent course website: https://www2.stat.duke.edu/courses/Fall19/sta711/
STA 601 is an introduction to Bayesian Statistics, though it is better if some Bayesian Statistics is already known. It follow's Peter Hoff's book and is taught by Alexander Volfovsky. 
STA 721 is a theory based linear models class (mostly based off of Plane Answers to Complex Questions) that has a strong Bayesian emphasis. Here is the most recent class website: https://www2.stat.duke.edu/courses/Fall19/sta721/
STA 732 is the only "frequentist" theory course. Classical statistical theory is definitely minimally developed in the curriculum.
STA 831 is a follow-up course to STA 601 and is purely Bayesian. There is no textbook the class follows.
So the courses are difficult enough, but many students don't learn much non-Bayesian statistics, and some basic topics such as study design or generalized linear models are almost completely ignored (and neglected often in the elective offerings). Students going to Harvard or Washington (I know someone going to the latter) are taught far more classical statistics topics but don't have as much formal Bayesian coursework.
 

I'm familiar with Duke biostats but not so much Duke stats so I'll let other people help answer those questions for you. 
I will say that rock climbing in the Triangle is pretty popular - we have quite a few gyms within 20 minutes of Duke and some great mountain climbs ranging from 1 to 3 hours away.  I would say that there's definitely a climbing culture here! (Feel free to message me if you want to know any other specifics about the Triangle area)

@ENE1 I climb too! I've been to a bunch of different gyms on the east coast, so those are my main frame of reference. I visited Triangle Rock (I can't remember which branch) and it was definitely one of the better gyms I've been to. I can't speak much to the rope climbing, but the bouldering was great! I'm pretty sure the gym was in a former big box store, so there was a LOT to climb and a lot of variety. They grade pretty hard too, much harder than most gyms I've been to. I only spent a few hours there, so I can't speak to how often they reset or the vibe of the community. The few people I did meet were really nice though. 

The rollercoaster is terrible, but it's not even March. Mid-March is when decisions really roll out. Initial waves have just been sent out or are being sent out now. Most schools don't send out other waves of acceptances until after they here from their first wave, that way they don't send out too many. 
You guys are fine. The waiting game sucks- but it happens year after year. Develop hobbies and find ways to keep yourself out of your email. That's what I did!
Best of luck.

I've got to say, being in this weird purgatory where rejections have been sent out and you haven't gotten any but at the same time you didn't get interviewed is pretty awful... just reject me already lol.

This is something I've heard a lot and I'm curious if this sentiment is more to emphasize that you can't build your research from coursework,  or if it's a commentary on the role of coursework in a PhD program. With that, what are the intended purposes of coursework in a PhD program? Would you ever advise choosing one program over another based on course offerings (holding all else roughly equal)?  
 
My impression is that the classes you take give you some more interaction with select faculty, inform your areas of interest, provide a level of basic/vocational training for when you are working on research, and they can be a lot of fun. I understand that coursework is not the purpose of a PhD, but sometimes I get the impression that people feel like it was of little importance to their PhD experience as a whole. 

@ENE1 A little bit! I'll just message you though. I doubt most people want to hear about climbing haha.

I graduated from Berkeley stats PhD - the courses are rigorous, but not crazy. I haven't really heard of PhD students dropping classes because they couldn't handle it.
We do have a pretty laid back set of requirements - no written qualifying exams, only an oral exam you take sometime between your second and fifth year which students never fail.
Anecdotally, I've heard Gelman is very difficult to work with (a Columbia PhD student volunteered that on my visit day)
Berkeley is really great, but I wouldn't stress too much - you can get a great education at any of those schools. Just find somewhere that you'll be happy!

I got accepted to University of Washington! So excited I'm actually shaking

I've been through the process a few times, and I think the only real way to lower the stress is to lower the importance that you assign to the whole process, which you can do in a few ways.
1) You already have some admissions.  Spend some time researching the programs you've gotten into, the city, where you might live.  Get excited about one of the opportunities you already have.
2) If you can't get excited about the schools you're already in, think about what you might do if you don't get in anywhere.  I'm sure as  a smart math-savvy grad, you'll have some other decent options of things you could occupy yourself with for a year.  Find a fun alternative. Convince yourself you don't actually need to go to grad school this year and you won't assign any importance to the process.  Life is long, and if you realize waiting a year won't be the end of the world, it removes the pressure.
I don't think you'll ever get rid of the curiosity and desire to check your email and see results, but you can at least lower the unnecessary stress.  I've worried myself sick about this stuff too many times and it's not worth it.  Your life will be fine regardless of these results and you really need to convince yourself of the truth of that.

You have an excellent GPA in Statistics/IE from one of the best schools in South Korea. I hardly think you have "no chance at all." However, you may need to calibrate your expectations a bit (e.g. Berkeley) since admissions is extremely competitive for international students. But being a strong student from Yonsei certainly gives you a great chance at schools like NCSU, TAMU, Purdue, Penn State, etc. I would target larger programs at big public universities and apply to a few "reaches."
I would just apply and see what happens. I mean... you miss 100% of the shots you don't take. Try to get very strong letters of recommendation. 

I am an international student who got interviewed by UW. POI asked a few easy questions about me and spent much time highlighting the advantages of UW and his recent research interest. The talk was warm.