Collaborative Research Team Project #05

Statistical Inference for Complex Surveys with Missing Observations

This project explores methods for analyzing high-dimensional data sets with missing values.

Research Category: Information Sciences

Why Study Complex Surveys?

Complex surveys play an important role in providing information for science and society. For the efficient use of this information, survey data must be reliable and representative. 

For the past three decades, one of the focal points in survey research has been how to handle missing data. Imputation is an increasingly used technique. However, it is notoriously difficult to impute multivariate data having an arbitrary missing pattern if the covariance structure must be preserved. Addressing this problem will be a major part of the planned research.

Areas of Exploration

Complex Survey Data

Includes collaborating with researchers at Statistics Canada and Westat on problems in large-scale, high-dimensional survey data with missing values. 

Techniques for Incomplete Data

Includes extending the techniques of fractional imputation and doubly robust methods to dealing with missing values in high dimensional data. 

Statistical Inference & Applications

Includes exploring the relatively new area of developing inference from incomplete functional survey data, with application to large functional data sets, such as electricity consumption data.

Solving Global Challenges

Research Team’s Goal

To carry out research and training in high-dimensional survey data analysis, with a focus on challenges in data collection resulting from non-response and missing values.

Related Events

Related Events

Spring School on Statistical Inference for Survey Data with Missing Observations | June 6-9, 2017 at the Fields Institute
Learn More

Statistical Inference for Complex Surveys with Missing Observations Seminar Series | 2015-2016

Seminars include:

  • Surveys from large datasets of functional data and estimation of the mean and median curve with full and missing data
  • Small Area Quantile Estimation
  • Validity and Efficiency in Analyzing Ordinal Responses with Missing Observations
  • Inferential Issues in the Presence of Imputation for Missing Survey Data
  • An Application of Small Area Estimation to the Labour Force Survey Data
  • Some Recent Topics on Informative Sampling
  • Empirical Likelihood Inference with Public-Use Survey Data

People Behind the Project

Project Team

David Haziza | Université de Montréal


Jean-François Beaumont | Statistics Canada

Michael Brick | Westat (Rockville, USA)

Hervé Cardot | Université de Bourgogne

Camelia Goga | Université de Bourgogne

Jiahua Chen | University of British Columbia

Jae-Kwang Kim | Iowa State University

Wilson Lu | Acadia University

Changbao Wu | University of Waterloo

Relevant Publications


Statistical Inference for Complex Surveys with Missing Observations is a Collaborative Research Team project. This program tackles complex problems through a three-year research and training agenda.

CANSSI offers approximately $200,000 for this type of project, which requires a team of faculty, postdocs, and students.