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
May 10, 2023 - Causal inference in statistics: why, what, and how
Presenters: Erica Moodie
Abstract: An introductory overview on the goals of causal inference, key quantities, and typical methods will be given for situations where an exposure of interest is set at a chosen baseline (“point exposure”) and the target outcome arises at a later time point, focusing on a binary outcome and continuous exposure. Using the potential outcomes framework, principled definitions of causal effects will be presented along with estimation approaches which invoke the no unmeasured confounding assumption.
Short bio: Erica E. M. Moodie is a Professor of Biostatistics at McGill University and a Canada Research Chair (Tier 1) in Statistical Methods for Precision Medicine. She obtained her MPhil in Epidemiology in 2001 from the University of Cambridge and a PhD in Biostatistics in 2006 from the University of Washington, before joining the faculty at McGill. Her main research interests are in causal inference and longitudinal data with a focus on precision medicine. She is the 2020 recipient of the CRM-SSC Prize in Statistics and an Elected Member of the International Statistical Institute. Dr Moodie serves as an Associate Editor of Biometrics and a Statistical Editor of Journal of Infectious Diseases. She holds a chercheur de merite career award from the Fonds de recherche du Quebec-Sante.
March 8, 2023 - "Positioning Household Surveys for the Next Decade: Data Integration and Inclusion"
Presenters: Talip Kilic and Papa Seck
Slides available here.
Recording available here.
Abstract: Established by the UN Statistical Commission in 2015, the Inter-Secretariat Working Group on Household Surveys (ISWGHS) aims to foster improvement in the scope and quality of social and economic statistics as delivered through national, regional and international household survey programmes. Three major objectives of ISWGHS are (a) fostering coordination; (b) promoting advancement and harmonization of survey methodologies; and (c) communicating the importance of household surveys.
As a collaborative group, ISWGHS is a platform to support the exchange of knowledge and experience among international agencies, national statistical offices, academic experts, the private sector and other key players. Such exchange will improve the flow of knowledge and experience among countries and assist in the scaling up of innovative approaches in countries with lower statistical capacity.
In guiding its support to countries in adopting innovative approaches and becoming more resilient to crises such as the COVID pandemic, ISWGHS prepared a paper on Positioning Household Surveys for the Next Decade (Position Paper). The webinar will focus on two of the eight technical priority areas identified by the Position Paper: Enhancing the interoperability and integration of household surveys and Designing and implementing more inclusive, respondent-centric surveys. The presentation will cover innovative approaches in these two areas, with examples from countries.
Short bios: Talip Kilic is the Program Manager in Data Production and Methods Unit Development Data Group at the World Bank. Talip’s research focuses on poverty, agriculture, and gender in low- and middle-income countries, as well as survey methodology to improve the quality, timeliness, and policy-relevance of household and farm surveys. Talip is originally from Istanbul, Turkey, and have a Ph.D. in Economics from American University in Washington, DC, and a Bachelor of Arts in Economics and International Relations from Knox College in Galesburg, IL.
Papa Seck is Chief, Research and Data Section at UN Women and co-Chair, Inter-Secretariat Working Group on Household Surveys. Since joining UN Women in 2009, he has led statistics and data work at UN Women. Prior to joining UN Women, Papa worked for UNDP as a statistics specialist, contributing to three global Human Development Reports. He holds a Master’s degree in Economics from Hunter College and is the co-editor of a book on the consequences of risk and vulnerability for human development.
December 8, 2022 - "Deep learning: opening the black box "
Presenter: Dr. Jennifer A. Hoeting, Hoeting Consulting
Slides Available here
Recording Available here
Abstract: Deep learning algorithms are often presented as black box algorithms. Many cartoon sketches of deep learning are available, but deep learning is rarely translated into the mathematical framework required by most statisticians to understand the topic. In this lecture we will open the black box and explore deep learning from a statistical viewpoint. In addition to an overview of the mathematics of deep learning, we will explore the types of problems when you might consider using a machine or deep learning algorithm and when traditional inferential statistical methods may be preferred. We will provide suggestions of how to get started applying machine and deep learning algorithms to your own data with links to recommended software. This lecture is intended for statisticians who want to learn about deep learning but have little previous exposure to it.
Jennifer A. Hoeting leads Hoeting Consulting. She is Professor Emeritus of Statistics at Colorado State University and Adjunct Professor of Statistics at University of California Santa Cruz. Hoeting's textbook, Computational Statistics (co-authored by Geof H. Givens), has been adopted as a course textbook at more than 120 universities in the US and over 30 other countries. Hoeting is an elected Fellow of the American Statistical Association and received the Distinguished Achievement Medal from the American Statistical Association’s Section on Statistics and the Environment.
September 21, 2022 - "An Introduction to Spatial Statistics with Applications to Disease Mapping"
Presenter: Dr. Alexandra M. Schmidt, Professor, McGill University, Canada
Slides and Resources available here.
Presentation Recording available here.
Abstract: 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.
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.
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…
Dr. Carolina Franco is a Principal Statistician at NORC at the University of Chicago, specializing in small area estimation (SAE). Prior to moving to NORC last year, Dr. Franco spent many several years doing research and consulting at the US Census Bureau, where she worked on important SAE programs used for the production of official statistics, including the Small Area Income and Poverty Estimates (SAIPE) Program, and the Voting Rights Act, Section 203 determinations. She also has extensive experience giving seminars on SAE and has taught SAE through the Joint Program in Survey Methodology at the University of Maryland. She has published several papers on different aspects of SAE.
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