Project description
Deriving meaningful data from heterogeneous and distributed datasets
Digitisation has led to the massive generation and collection of data, with great potential to benefit science, technology and social policy. Data, however, is often collected from multiple sources hastily and inexpensively without attention to standard experimental structure or formatting. From a statistical perspective, the challenge is how to extract significant data from such heterogeneous and distributed datasets. To address this, the ERC-funded HeDiStat project aims to develop novel statistical methodology and theoretical frameworks that consolidate various forms of data heterogeneity and measurement error. The four key areas will focus on accounting for sampling bias through semiparametric models, file matching issues through statistical optimal transport, rectifying errors due to missing data assumptions and upholding differential privacy.
Objective
Data is now collected at unprecedented scales across many industries, meaning that there is huge potential for evidence-based advances in science, technology and public policy. However, to harness this potential we must navigate repositories that are often a far cry from the idealised datasets, carefully collected and curated under perfect conditions, that are usually imagined when new statistical methodology is introduced. Data are often gathered quickly and cheaply, patched together from multiple locations, with limited regard to enforcing experimental standards. We may have the large sample sizes we desire, but there will be missing values, misaligned datasets, contamination and, depending on the sector, there may be noise added purposefully to satisfy individuals' and regulatory bodies' privacy concerns.
We propose to address such difficulties through the development of new statistical methodology and theoretical frameworks that explicitly incorporate various forms of data heterogeneity and measurement error. This will be divided into four main areas:
1. Accounting for sampling bias when a complete dataset is complemented by additional incomplete datasets. This will be studied through the lens of semiparametric theory for functional estimation.
2. Combining two or more datasets that record overlapping but distinct sets of variables, where few or no complete records of all variables are available. These file matching problems will be studied using new developments in statistical optimal transport.
3. Examining the effect of the violation of missing data assumptions. Here we will introduce techniques from robust statistics to mitigate the error due to misspecifying assumptions about sampling bias.
4. Securing individuals' private data through the intentional use of noisy measurement. Here we contribute to the growing field of differential privacy, specifically the user-level local variant, where distributed batches of observations are privatised simultaneously.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
CV4 8UW COVENTRY
United Kingdom
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