Grant: Discovery Grants Program - Individual
The University of British Columbia
$106,000.00
Bayesian Evidence Synthesis: Exploring the Limits of Limited data
FY 2023-24
Summary
The quantification of evidence contained in data is clearly central to most, if not all, scientific endeavours. Very often the available data are more limited than one would like, yet the need to quantify the evidence is no less pressing. A lot of statistical methodological research, including that proposed here, looks to develop methodologies which mitigate limitations of the data. Common examples of the limitations faced include important variables being measured poorly, or not at all, for some study units, and bias in the selection of study units. This proposed research work aims to further the understanding and implementation of Bayesian methods to quantify evidence from data. The Bayesian paradigm offers a principled scheme to manage the various uncertainties that lie on the route from data to scientific evidence.
Program
Discovery Grants Program - Individual
Recipient
Location
British Columbia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2019-03957
Name nom
Gustafson, Paul
Department département
Statistics
Organization id
2
Institution établissement
The University of British Columbia
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2019
Award amount
106000
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
3000
Research subject group en
Statistics and probability
Research subject group fr
Statistics and probability
Research subject en
Statistique et probabilité
Research subject fr
Statistique et probabilité
Application title
Bayesian Evidence Synthesis: Exploring the Limits of Limited data
Keyword
Bayesian methods; Causal inference; Evidence synthesis; Measurement error; Misclassification; Partial identification
Application summary
The quantification of evidence contained in data is clearly central to most, if not all, scientific endeavours. Very often the available data are more limited than one would like, yet the need to quantify the evidence is no less pressing. A lot of statistical methodological research, including that proposed here, looks to develop methodologies which mitigate limitations of the data. Common examples of the limitations faced include important variables being measured poorly, or not at all, for some study units, and bias in the selection of study units. This proposed research work aims to further the understanding and implementation of Bayesian methods to quantify evidence from data. The Bayesian paradigm offers a principled scheme to manage the various uncertainties that lie on the route from data to scientific evidence.
