Philippe Gagnon, Université de Montréal

Research

Bayesian statistics generalised linear models linear regression Markov chain Monte Carlo methods model selection (non)reversible jump algorithms outliers super heavy-tailed distributions

Latest funding

Robust and efficient statistical learning algorithms with applications in actuarial science
NSERC · 2020 · Principal investigator
$90,000
Robust and efficient statistical learning algorithms with applications in actuarial science
NSERC · 2020 · Principal investigator
$12,500

All 2 grants and awards

Robust and efficient statistical learning algorithms with applications in actuarial science

Principal investigatorNSERC2020Discovery Grants Program - Individual

Principal investigators: Gagnon, Philippe

Keywords: bayesian statistics; generalised linear models; linear regression; markov chain monte carlo methods; model selection; (non)reversible jump algorithms; outliers; super heavy-tailed distributions; variable selection; whole robustness

$90,000

Robust and efficient statistical learning algorithms with applications in actuarial science

Principal investigatorNSERC2020Discovery Launch Supplement

Principal investigators: Gagnon, Philippe

$12,500

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

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Applying for

Mention their NSERC grant from 2020

Subject: Prospective PhD student: Bayesian statistics Dear Professor Gagnon, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. Your research on bayesian statistics connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your NSERC-funded project "Robust and efficient statistical learning algorithms with applications in actuarial science" (2020), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]