Seminar Details

Many surveys (and other types of databases) suffer from unit and item nonresponse.
Typical practice accounts for unit nonresponse by inflating respondents’ survey
weights, and accounts for item nonresponse using some form of imputation. Most
methods implicitly treat both sources of nonresponse as missing at random.
Sometimes, however, one knows information about the marginal distributions of
some of the variables subject to missingness. In this talk, I discuss how such
information can be leveraged to handle nonignorable missing data, including
allowing different mechanisms for unit and item nonresponse (e.g., nonignorable
unit nonresponse and ignorable item nonresponse).
Bio:
Jerry Reiter is the Dean of the Natural Sciences and Professor of Statistical Science
at Duke University. His primary areas of research include methods for ensuring
data privacy, for handling missing and erroneous values, for combining
information across sources, and for analyzing complex data in the social sciences
and public policy. He is a Fellow of the American Statistical Association and a
Fellow of the Institute of Mathematical Statistics. He is the recipient of several
teaching and mentoring awards from Duke University, including the Alumni
Distinguished Undergraduate Teaching Award, the Outstanding Postdoctoral
Mentor Award, and the Master's of Interdisciplinary Data Science Distinguished
Faculty Award.