Canadian Stats Ph.D. Programs

[Statistique]

Hi there,

I just registered in the hopes that someone could help me out.

My question is this: How do the top Canadian programs for a Stats Ph.D. stack up against the American ones? I'm not using US World & News as an end-all, be-all, but I'd like to know roughly where the Canadian schools would fit in.

Specifically, I'm looking at: UToronto, UBC, Waterloo, Simon Fraser, and McGill. But that's just based on idle recommendations from profs; if I'm missing some good ones, I'd like to hear it.

Anyone with Canadian experience (eh?) care to give me a rough estimate for where these programs stand?

Thanks in advance!

[kash]

Hi there,

I just registered in the hopes that someone could help me out.

My question is this: How do the top Canadian programs for a Stats Ph.D. stack up against the American ones? I'm not using US World & News as an end-all, be-all, but I'd like to know roughly where the Canadian schools would fit in.

Specifically, I'm looking at: UToronto, UBC, Waterloo, Simon Fraser, and McGill. But that's just based on idle recommendations from profs; if I'm missing some good ones, I'd like to hear it.

Anyone with Canadian experience (eh?) care to give me a rough estimate for where these programs stand?

Thanks in advance!

The best people to ask would be your professors, i asked some of mine and they all agreed that Utoronto has a really strong program and it could compete with a top 10 school in the states.

[wakaka]

U of T is indeed very strong, but it is quite difficult for international students to get in. McGill is strong overall - most of the stat faculty have research interests related to biostat, some are interested in finance related topics, and some are interested in computation related ones. UBC is strong in biostat. Waterloo is strong in finance/actuarial science related topics.

In particular, McGill's stat program is in the same department as mathematics, so it may be more convenient to work on interdisciplinary subjects - please correct me if I am wrong.