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Coulombe, Janie

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Assistant Professor

Faculty of Arts and Science - Department of Mathematics and Statistics

André-Aisenstadt Office 4243

514 343-7977

Courriels

Courses

  • STT1700 H - Introduction à la statistique
  • STT3781 H - Laboratoire de statistique

Research area

I am interested in developing causal estimators with good statistical properties, that apply to observational data which carry special features. For instance, recently, I focused on irregular visits or observation of an important process in the estimation of a causal effect (some papers are available at the bottom, related to this topic).

Currently, I am particularly interested in developing more robust and efficient causal estimators. With Prof. Shu Yang (NCSU) we developed a quadruply robust estimator for the marginal causal effect (https://arxiv.org/abs/2304.08987) that is more flexible and efficient than the previous existing estimator for settings with irregular visits and confounding.  

I had the chance to work on the development of optimal treatment strategies and missing data imputation. Currently, I am interested in comparing multiple imputations of missing values to inverse intensity of visit weighting strategies (in progress).  

Eventually, I'd like to study tools to find optimal adjustment sets in causal diagrams (related to irregular visits) as well combining stochastic processes in continuous time with causal ifnerence.  

For more info, please see  https://janiecoulombestat.github.io  or papers can be found on Google scholar at https://scholar.google.com/citations?user=UmVoZQwAAAAJ&hl=fr&oi=ao .

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Papers on irregular visits

Coulombe, J., E. E. Moodie, S. M. Shortreed, et C. Renoux (2023). "Estimating individualized treatment rules in longitudinal studies with covariate-driven observation times." Statistical Methods in Medical Research (sous presse). 

Coulombe, J., E. E. Moodie, R. W. Platt and C. Renoux (2022). "Estimation of the Marginal Effect of Antidepressants on Body Mass Index under Confounding and Endogenous Covariate-Driven Monitoring Times." Annals of Applied Statistics 16(3): 1868--1890.

Coulombe, J., E. E. Moodie and R. W. Platt (2021). "Weighted regression analysis to correct for informative monitoring times and confounders in longitudinal studies." Biometrics 77(1): 162-174.

Coulombe, J., E. E. Moodie and R. W. Platt (2021). "Estimating the marginal effect of a continuous exposure on an ordinal outcome using data subject to covariate'-'driven treatment and visit processes." Statistics in Medicine 40(26): 5746-5764.

Student supervision Expand all Collapse all

Évaluation de la modélisation et des prévisions de la vitesse du vent menant à l'estimation de la production d'énergie annuelle d'une turbine éolienne Theses and supervised dissertations / 2015-04
Coulombe, Janie
Abstract
Following an internship with the company Hatch, we have access to datasets that are composed of wind speed time series measured at different sites accross the world and over several years. The wind speed engineers from Hatch are using these datasets jointly with Environment Canada databases in order to ascertain the wind energy potential of these sites and to know whether it is worth installing wind turbines there. For a few years, some companies are also offering mesoscale simulations of wind speed based on different environmental characteristics from the site we want to evaluate. We would like to know if it is worth paying for those mesoscale datasets and if they can be used to provide better estimations of the wind energy potential. Among other things, these data could be used to provide a better estimation of the long term mean wind speed. Since we already possess measured datasets, we will also use them to test, with statistical methods, the methodology currently used and the different steps leading to an estimation of the wind energy production. First of all, we will see what are the different methods that could be used to extrapolate wind speed to a wind turbine’s height and we will evaluate those methods with the mean squared extrapolation error. Also, we will study wind distribution modelling by the Weibull distribution and consider its variability over time. Finally, cross-validation and block bootstrap will be used to see whether we should use mesoscale data instead of wind data from Environment Canada or whether it would even be beneficial to use both kind of data to predict wind speed. In summary, the whole methodology used by wind speed engineers to estimate the energy production will be tested from a statistical point of view and we will attempt to propose changes in this methodology that could improve the estimation of the wind speed annual energy production.

Awards and Recognition

  • Prix Pierre Robillard Remis par la Société Statistique du Canada en reconnaissance à la meilleure thèse de doctorat dans un domaine en statistique pour une année donnée au Canada., 2022