Webinars



Webinars

Web-based training is becoming increasingly popular as a cost-effective alternative to live training courses. Let the training come through your computer so you don’t have to leave your work environment. If you work with a team of statisticians, register once and gather your colleagues in a conference room so you can view the training session together and maximize your professional development budgets


Joint CIRS-Statistics Without Borders (SWB) webinar series

September 21, 2022 - "An Introduction to Spatial Statistics with Applications to Disease Mapping"
Prenter: Dr. Alexandra M. Schmidt, Professor, McGill University, Canada
This webinar will provide an introduction to the analysis of spatially structured data. It will introduce Spatial Statistics and focus on the analysis of data that vary over a discrete set of indices (areal data) as, for example, the number of registered cases of Covid-19 across the boroughs of Montreal. We will discuss conditional autoregressive models and how to perform inference procedure under the Bayesian paradigm. At the end of the webinar, participants will have learned how to
• fit conditional auto-regressive (CAR) models;
• fit areal level data using CARBayes in R;
• create disease maps.


Slides and Resources available here.
Presentation Recording available here.
Short bio:
Alex Schmidt is Professor of Biostatistics and holds the endowed University Chair in the Department of Epidemiology, Biostatistics and Occupational Health (EBOH) at McGill University. She is an Elected Fellow of the American Statistical Association (2020) and an Elected Member of the International Statistical Institute (2010). She was awarded the Distinguished Achievement Medal (2017) from the American Statistical Association’s Section on Statistics and the Environment and the Abdel El-Shaarawi Young Investigator Award (2008), from The International Environmetrics Society.  



Presenter: Dr. Shirin Golchi, Assistant Professor, McGill University, Canada
July 20, 2022 - "An Introduction to Bayesian Inference"

Presentation recording is available here.
Slides and resources are available here.
Presenter: Dr. Shirin Golchi, Assistant Professor, McGill University, Canada

This webinar will provide an introduction to basic concepts in Bayesian inference. Topics that will be covered include essential components of Bayesian statistics, estimation and uncertainty quantification in single and multi- parameter linear and generalized linear models, as well as a brief introduction to Bayesian hierarchical modeling and Bayesian computation. The workshop will include examples of parametric inference in R using R-packages that rely on Stan (rstanarm and brms). At the end of this workshops participants will be able to: 1) Specify simple Bayesian models, 2) Make Bayesian inference in single parameter models, and 3) Fit linear and generalized linear models using rstanarm or brms.

Short bio
Dr. Shirin Golchi is an assistant professor in biostatistics with an interest and specialty in Bayesian modelling and computational methods. She looks for interesting problems where new statistical methodology together with efficient computational tools can help scientists answer important questions. She has worked on a variety of problems with applications in health sciences, physics, social sciences and mathematical biology.


May 11, 2022
- "An Introduction to Small Area Estimation"
- now available online via YouTube: https://youtu.be/8oXfYMSyqEc
-slides available here
-references available here

Presenter: Dr. Carolina Franco, Principal Statistician, NORC at the University of Chicago, USA


Small area estimation (SAE) techniques can lead to greatly improved estimates relative to direct survey estimates when there is a large number of domains of interest and a limited overall sample size, which is often the case in surveys. When successfully applied, SAE can dramatically reduce measures of uncertainty and provide estimates for domains with no survey data. It can allow for publishing of official estimates at lower levels of aggregation.  We will discuss the following topics: What is small area estimation (SAE)? What are the potential benefits of SAE? Examples of real applications of small area estimation; An introduction to area-level and unit-level models;; Discussion of frequently used software; Where to learn more…




Other webinar series: 

TIES Webinar Series (Environmental Statistics and Data Science)

American Statistical Association (ASA) Webinars


ASA's Committee on Career Development (CCD) Webinars

International Statistical Institute (ISI) Webinars





Webinars

Web-based training is becoming increasingly popular as a cost-effective alternative to live training courses. Let the training come through your computer so you don’t have to leave your work environment. If you work with a team of statisticians, register once and gather your colleagues in a conference room so you can view the training session together and maximize your professional development budgets


Joint CIRS-Statistics Without Borders (SWB) webinar series

July 20, 2022 - "An Introduction to Bayesian Inference"

Presenter: Dr. Shirin Golchi, Assistant Professor, McGill University, Canada

This webinar will provide an introduction to basic concepts in Bayesian inference. Topics that will be covered include essential components of Bayesian statistics, estimation and uncertainty quantification in single and multi- parameter linear and generalized linear models, as well as a brief introduction to Bayesian hierarchical modeling and Bayesian computation. The workshop will include examples of parametric inference in R using R-packages that rely on Stan (rstanarm and brms). At the end of this workshops participants will be able to: 1) Specify simple Bayesian models, 2) Make Bayesian inference in single parameter models, and 3) Fit linear and generalized linear models using rstanarm or brms.

Register here.

Short bio

Dr. Shirin Golchi is an assistant professor in biostatistics with an interest and specialty in Bayesian modelling and computational methods. She looks for interesting problems where new statistical methodology together with efficient computational tools can help scientists answer important questions. She has worked on a variety of problems with applications in health sciences, physics, social sciences and mathematical biology.


May 11, 2022
- "An Introduction to Small Area Estimation"
- now available online via YouTube: https://youtu.be/8oXfYMSyqEc
-slides available here
-references available here

Presenter: Dr. Carolina Franco, Principal Statistician, NORC at the University of Chicago, USA


Small area estimation (SAE) techniques can lead to greatly improved estimates relative to direct survey estimates when there is a large number of domains of interest and a limited overall sample size, which is often the case in surveys. When successfully applied, SAE can dramatically reduce measures of uncertainty and provide estimates for domains with no survey data. It can allow for publishing of official estimates at lower levels of aggregation.  We will discuss the following topics: What is small area estimation (SAE)? What are the potential benefits of SAE? Examples of real applications of small area estimation; An introduction to area-level and unit-level models;; Discussion of frequently used software; Where to learn more…




Other webinar series: 

TIES Webinar Series (Environmental Statistics and Data Science)

American Statistical Association (ASA) Webinars


ASA's Committee on Career Development (CCD) Webinars

International Statistical Institute (ISI) Webinars