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DanielWarlock

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DanielWarlock last won the day on September 28 2020

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  1. I saw you have posted multiple times. The thing is we cannot quite tell you without much more information. The major factor here is that international students' application success rate depends a lot on whether their undergrad schools are well-known to US schools. For instance, IIT, ISI are well-known to US schools. US PhD programs admit students from these schools regularly so they can compare you with students they admitted from the past as well as compare you with your peers at the same year. For instance, if you are from ISI and your grades are top at your program, you can apply to top 20
  2. I personally don't think advanced calculus on transcript would make a huge difference given that you have taken mathematical statistics I, II and theoretical statistics I, II. It seems to be more productive to take probability if you haven't. The graduate version should give you all the analysis background for studying statistics including basic measure theory, Lp spaces, convergence (stochastic) etc. But of course it's always good to learn more.
  3. Seems your main motivation of getting a doctorate is to teach at college levels. Have you considered apply to graduate school of education? Some statisticians (e.g. Prof. Luke Miratrix) are actually professors of education school who does "regular" statistical research. I imagine you will be a much better candidate at education schools if that also fits your eventual goal of getting a doctorate.
  4. @catarctica Are you taking classes with Prof. Sheldon Lin at UofT? He is a world-class actuarial, a professor in the statistics department, and had done his PhD in mathematical analysis. I can't think of a better person to resolve your conundrum. He is very friendly to undergrad/master students and knows uoft curriculum inside out (including Zhou's class and all those analysis classes) -- he used to spend an hour discussing my application with me even though he barely knows me. You should try to talk to Prof. Lin and listen to what he says. He should know as an actuarial, where, who and what y
  5. Regarding the objections, I must reiterate that it depends on (i) math maturity (ii) how that class is taught at that particular year. The difference could literally be 6 hour/week v.s. 60 hrs per week for the same class with different instructors. Same thing goes with one's math maturity. From what I know, if Zhou Zhou or Rosenthal still teaches grad probability at UofT, it might be very doable. I heard that Zhou Zhou is drier/technical but Rosenthal on the other hand should stick to his book "A first look..." and has a reputation of being lenient and less technical. Take a read a
  6. I don't think it's impossible but rather depend on OP's "math maturity" and how these classes are taught at that particular year. OP is an actuarial not a musician. Actuarial studies is a mathematics degree at Waterloo. They do already know a great deal about theoretical things like SDEs. With certain math maturity, one can certainly take these de factor self-contained classes that tend to be taught from scratch even at graduate level. Every year, a couple sophomores or even freshmen take grad probability and grad real analysis with us. Last year we even had a junior as teach fellow for
  7. First, you need to tell us what school you went to and what percentile you ranked there in your cohort. Cornell, CMU, Duke, UPenn are all ranked at 101~150 in math but these schools are much more highly regarded compared to Hunan University, Chongqin univeristy from China which are ranked similarly on ARWU math. Whether you school is known and most importantly whether students from your program are admitted regularly to good PhD programs in US makes a huge difference. For example, indian statistical institute (ISI), Zhejiang Univeristy sent many students top phd programs but are ranked at 100-
  8. I doubt that one course in real analysis will change things drastically. I had overlapping courses with actuarial students at UofT including the "Elements of analysis": MATH 336 H1. This is the real analysis class for actuarial students at uoft and I'm worried you may take it. Don't! Take MATH 357H1 instead. I also had complex variable (334) btw. Got 100 in both of these classes --no help to my application at all. The truth is that most people in those classes are definitely not math-savvy and have no clue so the instructor has to go extremely slow and review calculus stuff all of the time. Th
  9. Completely agree with this assessment. I went to U of T for my undergrad and also had background from finance. I did my masters at Harvard with full A's in standard phd sequence (math stats, probability). My GPA is much better (near 4.0), with strong letters. Still I was rejected at schools at the rank of ~50, e.g. University of Florida as well as mid-ranged schools such as UWM. A major flaw is my math background which is still stronger than yours. The point is mid to low ranked schools care A lOT about math abilities such as real analysis but I don't have it. You are definitely NOT safe
  10. I would recommend just taking same courses with PhD students at Stanford (probability, theory stats, and applied stats) and focus on doing research. If you could do PhD sequence, there is no concern about whether or not if you have real analysis. And this saves you tons of time when you become a phd because you have already done these classes. I think you failed last time because you have no relevant research experience in statistics--that *in principle* is fine but you are competing against people who have done cutting-edge stuff in stats for 2+ years in their undergrad and have accumul
  11. Given your interest, I think Harvard is best fit. Imai has affiliation at Kennedy school, Neil Shephard is affiliated at economics. Murphy is also a big name here doing causal inference and reinforcement learning affiliated to CS department. There is no problem that you seek additional advisors at MIT or other Harvard departments. You can easily find someone at MIT to supplement for (3) (4). Everyone is saying Stanford stats but they are mainly about highly mathematical/theoretical high-dimensional stats and probability theory. So I guess you will need to go to their CS department to find advi
  12. Both schools are focused around the theme of high-dimensional stats. But risking oversimplification, a quick summary of their difference is: CMU is more "CS"; UChicago is more "mathematical". If you consider yourself more of a mathematician/probabilist, go to Chicago. If you consider yourself a computer scientist who looks at more applied stuff, then go to CMU. I will now further explain what I mean. CMU focuses more heavily on more applied, interdisciplinary stuff like neurosciences, astrostatistics, social sciences and yes sports analytics. Of course, most of these are done under the
  13. Again, if you are talking about statistics, then I would say they are both on the same level. But the reputation of UChicago is better if you talk to someone who is not a statistician
  14. Very interesting story. Maybe he is not dead-set on getting a PhD? Yale stats is actually very good. With guys like Harrison Zhou, Zhou Fan and Van Vu, I can even see one choosing Yale over Stanford when admitted to both. Not to mention the tremendous cost of money and time and the uncertainty of actually getting into Stanford (or even Yale itself) 2 years later. Sounds like a terrible decision.
  15. I personally would definitely go to UChicago. It is the most reputable and give you a leg up finding jobs in not only data analysts/statistician roles but also in finance, consulting, SDE etc whereas Duke, UNC, UW Maddison are strong in statistics but not as strong in other things (like finance). That said, if you would like to do a PhD, ETH Zurich may also be a good option because European masters, as far as I know, is research-based and you could very likely transfer into their very excellent PhD program and finish much faster.
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