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StatsG0d last won the day on May 24

StatsG0d had the most liked content!


About StatsG0d

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  • Location
    United States
  • Application Season
    Not Applicable
  • Program
    Biostatistics (faculty)

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  1. I agree with @bayessays. Most students will basically have taken up to Real Analysis and not much extra. Some students even get admitted without RA, although this is becoming more rare. I do think that the more you have, the stronger your application will be. So if you're fighting for the top programs, it definitely won't hurt to take more math (provided you earn good grades in those classes).
  2. I think it matters more that you have letters that attest to your mathematics ability than your statistics ability.
  3. Your math background is extremely broad, and you've received a bunch of high grades in graduate level math courses. That said, it's hard to know how adcoms will view you because of your undergrad GPA and getting low grades in calculus / linear algebra, which are the two most important prerequisite courses in statistics / biostatistics. You will have to address these discrepancies in your SOP. Given your math background, I recommend that you take the math subject GRE. I think you could do quite well on it, and if you do, no one is going to care about your undergrad grades as a lot of mater
  4. I don't think biostats is more competitive than stats. Probably more the contrary. Very few students have any relevant research experience coming in. To me, it sounds like your research experience does relate to biostats, as high dimensional data is common (e.g., genomics) and so is shrinkage (e.g., Bayesian analysis). To formulate your interests, I recommend you read some papers or just google some fields that are popular in biostatistics and relate to the branch of statistics you are interested in, e.g. Computation - genomics Machine learning - precision medicine Spati
  5. If you are interested in applied statistics, I don't think UPenn would be a very good fit. The other schools on your list seem reasonable (maybe not Iowa state though). Have you considered biostatistics? I think you would have a very strong profile.
  6. Although your GPA is quite low, you have several published manuscripts and your math background is very deep. I think you have a shot at the schools in the 5-10 range. I would apply to all of those (replacing your least 5 favorite schools in the above), and see what happens. Most of the programs you listed are very unknown. It's not clear they would want someone who appears equipped to be a methodologist, as many of these programs are applied. If applications are your interest, I would clearly state this on your SOP, because based on your math background I would think you would want to b
  7. I agree if the OP meant stats departments then they should apply to those. I assumed they meant biostats
  8. I apologize if I confused you. I didn't mean the "applied real analysis" course specific--just a general course in what most institutions call real analysis. Advanced Calculus is the way to go. Perhaps that's the confusion surrounding your advisor's saying no one takes it.
  9. The problem with NYU and Northwestern is that both programs are highly selective relative their ranking. This is typically because some international students care more about university prestige rather than departmental prestige. The other schools don't seem bad. If being in a somewhat large metro area is important to you, you can apply to some of the larger programs in those areas such as NCSU (downtown Raleigh), OSU (Columbus, OH), Minnesota (Minneapolis, but cold). Some of the larger state schools in smaller areas tend to have really sophisticated bus systems that can get you around r
  10. I think you're selling yourself really short. First, I wouldn't bother applying to UCSD, TAMU, Rutgers, or Rice. NCSU doesn't have a PhD in biostatistics, although they do have a Biostatistics concentration in their Statistics department. Also, not sure if UW refers to Wisconsin or Washington. You're competitive for any of the biostats programs in the top-5. In fact, I would be shocked if you didn't at least get into one of UNC or Michigan. I would say apply to all the top-7 biostats programs and maybe those Canadian schools if you're interested. I'd maybe add McGill if you're interested
  11. It's really hard to say. Typically, a lot of non-US universities have sort of a pipeline with various US-based PhD programs. If yours doesn't have that, it's honestly a crapshoot. Your best bet is to apply to schools that tend to admit a lot of international students (e.g., University of Florida comes to mind). There are probably many others, but that's the one I know for sure tends to admit many international students. Browse this forum--I'm positive others have mentioned other programs.
  12. You should check out mathematicsgre.com, as this forum is really biased more towards statistics than math.
  13. It's possible (and highly likely) that the other students had already taken the course (e.g., in undergrad). You definitely need to take real analysis for stats. You *might* be able to get away without taking it if you're doing biostats (although, this is becoming less common at the top 5-7 programs, as the field is becoming more competitive). Even if you managed to get into a program without taking analysis, you would have wished that you'd taken it. Even in Casella-Berger level Math Stats, real analysis is very useful for making mathematically rigorous arguments / proofs. I feel like yo
  14. This is exactly what I was trying to say regarding Kosorok vs. Hudgens and causal inference / precision medicine above, but is a more elegant and general answer. Couldn't agree more. I totally agree with you, but I figured I'd let the OP decide / figure out which programs are suitable.
  15. It's a fair question. To me, most of causal inference is concerned with identifying a population average treatment effect (typically not adjusted for covariates), while precision medicine is mostly concerned with which treatment for which individual at what time. Most of traditional causal inference utilizes classical statistical techniques (e.g., regression, GLM, etc.), albeit with some adjustments to account for confounding. In causal inference, it's really important to prove things such as consistency and asymptotic normality. In precision medicine, a lot of the methods are more machin
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