COLLEGE OF ARTS AND SCIENCES Department of Mathematics and Statistics
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Mathematics Colloquium: Regression Trees and Douglas Firs: Improving Federal Statistics with Machine Learning Methods

2019-11-14

4:10pm Location TBA

Kelly McConville

Reed College

For survey stats practitioners, it sometimes feels like the sky is falling. Response rates are declining. Data collection costs are increasing. Federal budgets are shrinking. But, with the advent of big data and advancements in technology, a wealth of new data sources, such as satellite imagery, are available to supplement survey data. And, flexible, predictive models, such as regression trees and elastic net regression, can be an effective tool to combine these different data sources and to produce more efficient estimators. Drawing on my collaborations with the U.S. Bureau of Labor Statistics and the U.S. Forest Inventory and Analysis Program, we will explore the utility of predictive models in survey estimation.