Date/Time
Date(s) - 08/07/2024 - 10/07/2024
All Day
Category(ies) No Categories
CANSSI-CRT Workshop on Modern Methods in Survey Sampling
Date and time
Location
University of Ottawa
75 Laurier Avenue East Ottawa, ON K1N 6N5 Canada
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 dhaziza@uottawa.ca.
Program
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 | Daniel 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