ecomath

Hello everyone, I'm looking for some advice about how I can prepare for a PhD in Statistics / Biostatistics coming from another field (Economics). I have a BSc in Economics and Mathematics where I took undergraduate probability and econometrics, along with some relevant math courses like linear algebra, real analysis and non-linear optimisation. I also have an MA in Economics where I took the first year PhD sequence in econometrics and the mathematical statistics sequence from the Statistics Department. In terms of research experience, I have had several empirically-oriented RA jobs and I have a co-authored publication in a very highly ranked economics field journal. I'm not particularly interested in economics and path-dependence has taken me this far in the field. I'd like to make the move into doing causal inference research with biomedical applications (nothing more specific than that at the moment). I'm just not sure what preparation I should have before sending out applications. I'm planning to take the measure-theoretic probability sequence this coming year and maybe a Bayesian statistics course from the School of Public Health (since econometrics is heavily frequentist). Is there other coursework that I should be trying to pick up? Should I push hard to get an RA job from someone in the Biostatistics Department? Or maybe try to land work in a teaching hospital? At the moment all my letters, while strong, will be from economists. Any advice would be greatly appreciated! Thanks in advance!

For those curious, I did some digging into this programme. U of T's School of Public Health is very new and the Biostatistics Division is accordingly very small with only 4 tenure-track faculty. As I understand it, PhD students are often advised by professors in the Statistics Department, which has a cohort of about 5 or 6 people doing biostatistics research, and by status-only professors in the surrounding research hospitals. In terms of placements, over the last 10 years (N = 21), 30% went on to be university faculty, some at U of T, and 40% went on to research institutes and teaching hospitals. The rest are in industry and government. This information isn't easy to get hold of, so hopefully some fellow Canadians will find it useful.

Okay, I applied to U of T Stats as well and I think I have a good shot of getting in. Maybe it's better to do a Stats PhD and collaborate with the Biostats Department.

So not having heard from Michigan could signal basically anything at this point? Or do we think it's slightly more good than bad given that rejections have started coming out?

Hello, I have received an offer of admission to U of T's Biostatistics PhD. I know that U of T is surrounded by a big medical research hub and U of T itself has strong medicine and biology departments, so it seems like it has the ingredients of a good program. It's also close to my family so I have personal reasons for wanting to attend. The problem is that I can't find much info about the Biostatistics PhD. Does U of T have a good reputation in the field? Does anyone know what its placements are like in academia and/or industry? It seems to have a focus on statistical genetics, which I'm interested in, but I don't know much more about its research strengths. Sorry for the naive questions. Thanks in advance!

Same. I haven't heard anything.

Perish the thought! I just want to hear back from 1 place so I have something set in stone.

As a quick update, I got an A+ in my graduate Mathematical Statistics I course and an A in my PhD Econometrics I course. Do you think it's worth it to try and send an updated transcript to the places I applied? And does this change my chances in any significant way?

A 3.3 might not be too bad if admissions committees know that the average GPA at U of T is generally much lower than comparably ranked schools in the States. 3.2+ at U of T is Distinction, so you should emphasise that in your statement somewhere.

Thanks for the input! I'm tentatively interested in biological applications (mostly from a "wow that seems both cool and useful" point of view), but I'm not sure whether it's a good idea to pigeonhole myself by starting a Biostatistics PhD on that basis. Is it considerably harder to get into biological applications if you're in a pure Statistics PhD? Apologies if my ignorance is showing here. I'm not sure. Oxford allows direct entry from undergrad, and Imperial wants an MA in a subject related to Statistics (I figure Economics is probably close enough, given the courses I'm taking).

Thanks for the recommendation. I'll definitely take a look at that.

Great, I'll pass that along. Thanks again!

Thanks for the feedback! I really appreciate it. No, Brown shouldn't be on there. That's a holdover from when I was planning to apply for Economics PhDs. I'll edit it out. If I can ask one more question, what sort of things will admissions committees be looking for in letters of recommendation? One of my letter writers has intimated that they're not sure what to emphasise in a letter to a Statistics department.

Hi everyone, I'm making the transition from economics to statistics. My main problem is that I don't know many statisticians, so I have almost no clue how competitive my profile is. I would be eternally grateful if you would take a look at let me know whether my list of schools is too ambitious, too safe or just right. Thanks in advance! Undergrad Institution: Top 3 Canadian Major: Mathematics and Economics GPA: 3.7 (top 5 - 10%) Masters Institution: Top 3 Canadian (same as above) Major: Economics GPA: In progress Honor & Awards: Dean's List each year. I've also won a few miscellaneous awards for grades in mathematics and economics coursework, and two undergraduate research awards. Type of student: British citizen, Canadian permanent resident GRE: Q 165 V 161 AW 5.0 Courses: Undergraduate: Calculus I (A), Calculus II (A), Linear Algebra I (A), Linear Algebra II (B-), ODEs (A), Graph Theory and Combinatorics (A+), Real Analysis at Baby Rudin level (A+), Dynamical Systems (A+), Nonlinear Optimisation (B+), Linear Optimisation (A+), Econometrics I (A), Econometrics II (A) Graduate (all ongoing): Mathematical Statistics I, Mathematical Statistics II, PhD Econometrics I, PhD Econometrics II (Research) Experience: In my third year I did full-time summer research and part-time research during the school year. The research was in the economics department and related to game theory. In my fourth year I started doing part-time research with a group of economics professors. This led to full-time summer research, and has now turned into a co-authorship for a paper which has been invited to a top economics journal (this is very uncommon in economics, if that matters). The research is on labour market dynamics and is heavily econometrics-oriented. I also wrote my university's equivalent of an undergraduate thesis, and did very well on it. It was an empirical paper; nothing too fancy methods-wise, though. Letters of recommendation: The professor I did game theory research with in my third year. This should be somewhere between very strong and incredibly strong. One of the professors I'm doing research with for the co-authorship (since I've worked more closely with him on the estimation side of the paper). This should be incredibly strong. I'm getting a joint letter from the other two professors I'm co-authoring with. This should also be incredibly strong. Worries: I don't have a strong background in statistics. I made the decision to switch to statistics very recently, and before then I had been preparing to go to graduate school in economics. None of my letter-writers are statisticians. Although I know that I love research and I find statistics absolutely fascinating, I don't have a strong grasp on what research in statistics looks like, so my statement of purpose will probably be very vague. Tentatively, I think I would like to move over into something related to biology. The stuff going on in statistical genetics or brain imaging (from what little I know of it) sounds incredibly cool. List of schools: University of Oxford University of Toronto University of McGill University of British Columbia University of California, Berkeley Carnegie Mellon University Duke University Imperial College London Boston University University of Michigan - Ann Arbor University of Washington