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CANSSI-CRT Workshop on Modern Methods in Survey Sampling

July 8 | 8:30 amJuly 10 | 1:00 pm

Modern Methods in Survey Sampling

About This Event

Complex surveys play an important role in providing information for policy makers and the general public as well as many scientific areas, such as public health and social science research. The objective of this workshop is to take stock of new developments in the field of survey data, to bring together some of the most active researchers in the field, and to identify the current challenges. The workshop is the final activity of a three-year Collaborative Research Team project funded by the Canadian Statistical Sciences Institute. The project is titled Modern Methods in Survey Sampling. The workshop will cover a range of topics, including

  • Machine learning methods
  • Data integration techniques
  • High-dimensional data
  • Small area estimation

For more information, please contact David Haziza at


This three-day event will take place at the University of Ottawa in Ottawa, Ontario, Canada. Registration for the event is C$250 and includes the cost of all lunches and coffee breaks.

A limited number of no-cost guest passes are available for graduate students with limited funding. Please contact David Haziza at for details.



Building location to be announced.


Residence rooms for graduate students: The organizers have arranged free accommodation for graduate students in the University of Ottawa’s Rideau Residence at 290 Rideau Street.

The residence rooms have two double beds and can accommodate up to four people. They come with a private bathroom, a mini-fridge and microwave, and air conditioning.

If you would like to reserve a residence room, please contact David Haziza at

Hotel accommodations: We have reserved a block of rooms at the Novotel Ottawa City Centre (Novotel Ottawa) at a reduced rate. The reduced rate is 175 Canadian dollars per night + taxes.

You can make a reservation using three different modes (see below). What’s important is that, regardless of the mode you choose, you need to use the following code to get the reduced price: 1265702

Option 1: You can reserve a room by email. Please write to and mention the code.

Option 2: You can reserve by phone at 001-613-230-3033, but don’t forget to mention the code.

Option 3: You can use the following link When making the reservation using this link, please select “attendee” to continue.


Monday, July 8, 2024

8:15 – 8:45 Registration

8:45 – 9:00 Introductory remarks

Chair: David Haziza

9:00 – 9:40 Fitting Classification Trees to Complex Survey Data | Jean Opsomer, WESTAT

9:40 – 10:20 Bayesian Tree Models for Data from a Complex Design | Daniell Toth, U.S. Bureau of Labor Statistics)

10:20 – 10:50 Coffee break | Session in honour of J.N.K. Rao

10:50 – 11:20 Permutation Tests Under a Rotating Sampling Plan With Clustered Data | Jiahua Chen, University of British Columbia

11:20 – 11:50 Optimal Predictors of General Small Area Parameters Under an Informative Sample Design | Isabel Molina, Universidad Complutense Madrid

11:50 – 12:20 Bayesian Empirical Likelihood Methods for Complex Survey Data | Changbao Wu, University of Waterloo

12:30 – 14:00 Lunch

Chair: Changbao Wu

14:00 – 14:40 Random Forests and Mixed Effects Random Forests for Small Area Estimation of General Parameters | Nikos Tzavidis, University of Southampton

14:40 – 15:10 Use of Random Forests in Small Area Estimation | Kevin Bosa, Statistics Canada

15:10 – 15:40 Coffee break

15:40 – 16:20 Debiased Calibration Estimation Using Generalized Entropy in Survey Sampling | Jae-Kwang Kim, Iowa State University

16:20 – 17:00 Variance Estimation for Survey Estimators Based on Statistical Learning Models| Mehdi Dagdoug, McGill University

17:00 – 17:30 Small Area Estimation with Random Forests and the LASSO | Victoire Michal, McGill University

Tuesday, July 9, 2024

Chair: Changbao Wu

9:10 – 9:50 Weight Smoothing via Design Modeling in Complex Surveys | F. Jay Breidt, NORC at the University of Chicago

9:50 – 10:30 Optimal Transport Methods in Survey Sampling | Yves Tillé, Université de Neuchâtel

10:30 – 11:00 Coffee break

11:00 – 11:40 Combining Probability and Non-probability Samples Using Semi-parametric Quantile Regression and a Non-parametric Estimator of the Participation Probability | Emily Berg, Iowa State University

11:40 – 12:20 Some New Developments on Likelihood Approaches to Estimation of Participation Probabilities for Non-probability Samples | Jean-François Beaumont, Statistics Canada

12:30 – 14:00 Lunch

Chair: David Haziza

14:00 – 14:40 Statistical Methods for Sampling Cross-classified Populations Under Constraints | Louis-Paul Rivest, Université Laval

14:40 – 15:10 Logistic Regression on Linked Data from a Secondary Analyst Perspective | Goldwyn Millar, Statistics Canada

15:10 – 15:40 Coffee break

15:40 – 16:20 Design-based Conformal Prediction for Survey Sampling | Jerzy Wieczorek, Colby College

16:20 – 17:00 Improving Estimates from the Survey on Household Income and Wealth Using Administrative Data with Measurement Error via Structural Equation Models | Giovanna Ranalli, Università degli Studi di Perugia

17:00 – 17:30 Inference from Nonrandom Samples Using Bayesian Machine Learning | Yutao Liu, Boehringer Ingelheim

Wednesday, July 10, 2024

Chair: Changbao Wu

9:10 – 9:50 Data Integration with Nonprobability Sample: Semiparametric Model-assisted Approach | Sixia Chen, University of Oklahoma

9:50 – 10:30 QR Prediction for Statistical Data Integration | Camelia Goga, Université de Bourgogne Franche Comté

10:30 – 11:00 Coffee break

11:00 – 11:40 Inference for Big Data Assisted by Small Area Methods: An Application on SDGs Sensitivity of Enterprises in Italy | Gaia Bertarelli, Ca’ Foscari University of Venice

11:40 – 12:10 Generalized Least Squares in Non-monotone Missing Data | Caleb Leedy, Iowa State University

12:10 – 12:40 Propensity Score Weighting with Post-treatment Survey Data | Wei Liang, University of Waterloo

12:45 Lunch


July 8 | 8:30 am
July 10 | 1:00 pm


University of Ottawa
75 Laurier Avenue East
Ottawa, Ontario K1N 6N5 Canada
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