[gov3009-l] Applied Stats this Wednesday: Krista Gile (UMass Math)

Tess Wise tesswise at gmail.com
Mon Mar 31 09:09:04 EDT 2014

Hi All --

Our speaker this Wednesday at Applied Stats will be Krista Gile who is a Professor in the Department of Mathematics and Statistics at the University of Massachusetts. She will be giving a talk entitled New methods for inference from Respondent-Driven Sampling Data. The abstract is included below.

Professor Gile's research focuses on developing statistical methodology for social and behavioral science research, particularly related to making inference from partially-observed social network structures. Most of her current work is focused on understanding the strengths and limitations of data sampled with link-tracing designs such as snowball sampling, contact tracing, and respondent-driven sampling.

As per usual, the talk will be held at 12 noon in CGIS K354. Lunch will be served.

I hope to see you all there!


New methods for inference from Respondent-Driven Sampling Data

Respondent-Driven Sampling is type of link-tracing network sampling used to study hard-to-reach populations.  Beginning with a convenience sample, each person sampled is given 2-3 uniquely identified coupons to distribute to other members of the target population, making them eligible for enrollment in the study. This is effective at collecting large diverse samples from many populations.
Current estimation relies on sampling weights estimated by treating the sampling process as a random walk on the underlying network of social relations.  These estimates are based on strong assumptions allowing the data to be treated as a probability sample. In particular, existing estimators assume a with-replacement sample with an ideal initial sample. We introduce two new estimators, the first based on a without-replacement approximation to the sampling process, and the second based on fitting a social network model (ERGM), and demonstrate their ability to adjust for biases due to the finite population and initial convenience sample.  Our estimators are based on a model-assisted design-based approach, using standard errors based on a parametric bootstrap.  We conclude with an application to data collected among injecting drug users, including extension to observable features of the sampling process.
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