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Part 2 of a multi-part workshop series on record linkage

Linear Regression With Linked Data: a Workshop with Emanuel Ben-David and Martin Slawski

a Workshop

Thursday, 8/22/2019, 9:00am to 1:00pm.   ARCHIVED EVENT

Location: Room 1430. 426 Thompson St. Ann Arbor, MI 48104

The PDHP workshop series resumes August 22nd with Part 2 of our ongoing Record Linkage series: Linear Regression With Linked Data. This half-day workshop, conducted by Emanuel Ben-David (of the US Census Bureau's Center for Statistical Research and Methodology) and Martin Slawski (of George Mason University), is geared toward population researchers, computational social scientists, statisticians, and data scientists of all experience levels.

Topics include:

• Overview of record linkage and entity resolution
• Impact of linkage error on regression analyses of linked data files
• Linkage error adjustment and correction methods (including regression techniques and optimal matching)
• Hands-on training and practice of these techniques using R software

The workshop is free of cost and open to the public. Please share widely.

The Population Dynamics and Health Program (PDHP) provides resources and services that support innovative approaches to data collection and analysis and the development of early-career population scientists, as well as research on significant and emergent issues in population dynamics and health.


Emanuel Ben-David is a Research Mathematical Statistician at the Center for Statistical Research and Methodology. His fields of interest include: Graphical & Causal Methods; Statistical Learning; Bayesian Statistics; Optimization Theory.

Given his background in both Statistics and Computer Science, Martin Slawski is interested in tackling problems arising at the interface of the two fields, an area that is nowadays often referred to as "Data Science." In his research, Slawski studies computationally tractable methods that yield compact representation of high-dimensional data. One recent focus involves randomized methods of dimensionality reduction and the associated computational-statistical trade-offs. Slawski also enjoys working on applications in interdisciplinary teams, in particular on problems involving biological data from high-throughput experiments.

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