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Grant: Discovery Grants Program - Individual

Université d'Ottawa

$42,000.00

Robust inference for complex survey data

EF 2024-25

Résumé
Complex surveys play an important role in providing information for policy makers and the general public as well as many scientific in other areas, such as public health and social science research. Since the 1950's, National Statistical Offices (NSO) have been using probability sampling methods and inferences were conducted with respect to the so--called design--based framework, which can be viewed as a model--free framework. In the last decade, the role of models in survey sampling has become more and more important. This shift of paradigm can be explained by three main factors: (i) decreasing response rates; (ii) high data collection costs and (iii) the proliferation of non--probability data sources that include web survey panels and satellite information. The primary objective of this project is to propose new strategies for addressing these challenges through four broad projects: (a) Multiply robust estimation procedures in the context of sample matching and propensity score estimation. (b) Multiply robust imputation procedures for complex parameters including distribution function and quantiles. (c) Outlier--resistant methods for small area estimation and multiply robust estimators of finite population means and average treatment effects; (d) Development of strategies for chasing nonrespondents and selective editing using the conditional bias of a unit. Multiply robust procedures make use of multiple outcome regression models and/or multiple propensity score models. A procedure is said to be multiply robust if it remains consistent when all but one of the models are misspecified. The first two projects strive to provide attractive strategies and solutions to the two seemingly separate but entangled problems on missing data problems and statistical matching. The third project attempts to develop estimation procedures that exhibit low mean square errors in the presence of influential units, which are common in business surveys. The final project will provide methods for deciding which respondents to chase in the context of unit nonresponse and which unit should undergo editing in a context of selecting editing. All four broad projects outlined in the proposal will involve training of graduate students at both master's and PhD levels and of postdoctoral fellows.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
RGPIN-2019-05891
Name nom
Haziza, David
Department département
Mathematics and Statistics - Mathématiques et statistiques
Organization id
28
Institution établissement
Université d'Ottawa
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2019
Award amount
42000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1508
Committee name en
Mathematics and Statistics
Committee name fr
Mathématiques et statistique
Area of application code
1200
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Advancement of knowledge
Area of application fr
Avancement des connaissances
Research subject code
3003
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Survey methodology
Research subject fr
Techniques de sondage
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Robust inference for complex survey data
Keywords
survey sampling; missing data; non probability sampling; variance estimation; multiply robust inference; outlier-robust inference; statistical matching; instrumental variables; calibration
Application summary
Complex surveys play an important role in providing information for policy makers and the general public as well as many scientific in other areas, such as public health and social science research. Since the 1950's, National Statistical Offices (NSO) have been using probability sampling methods and inferences were conducted with respect to the so--called design--based framework, which can be viewed as a model--free framework. In the last decade, the role of models in survey sampling has become more and more important. This shift of paradigm can be explained by three main factors: (i) decreasing response rates; (ii) high data collection costs and (iii) the proliferation of non--probability data sources that include web survey panels and satellite information. The primary objective of this project is to propose new strategies for addressing these challenges through four broad projects: (a) Multiply robust estimation procedures in the context of sample matching and propensity score estimation. (b) Multiply robust imputation procedures for complex parameters including distribution function and quantiles. (c) Outlier--resistant methods for small area estimation and multiply robust estimators of finite population means and average treatment effects; (d) Development of strategies for chasing nonrespondents and selective editing using the conditional bias of a unit. Multiply robust procedures make use of multiple outcome regression models and/or multiple propensity score models. A procedure is said to be multiply robust if it remains consistent when all but one of the models are misspecified. The first two projects strive to provide attractive strategies and solutions to the two seemingly separate but entangled problems on missing data problems and statistical matching. The third project attempts to develop estimation procedures that exhibit low mean square errors in the presence of influential units, which are common in business surveys. The final project will provide methods for deciding which respondents to chase in the context of unit nonresponse and which unit should undergo editing in a context of selecting editing. All four broad projects outlined in the proposal will involve training of graduate students at both master's and PhD levels and of postdoctoral fellows.