DigitalCollections@ILR

In this paper, we propose a statistical framework that generalizes the classical logit model to predict multiple responses (i.e., multi-label classification). We develop an effective implementation based on boosting and ...

This is the annual report by the Cornell node of the NSF-Census Research Network to NSF for 2013.

This is the annual report by the Cornell node of the NSF-Census Research Network to NSF for 2015.

Social science researchers increasingly make use of data that is confidential because it contains linkages to the identities of people, corporations, etc. The value of this data lies in the ability to join the identifiable ...

Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based ...

Using earnings data from the U.S. Census Bureau, this paper analyzes the role of the employer in explaining the rise in earnings inequality in the United States. We first establish a consistent frame of analysis ...

Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we ...

This is an excerpt from a prior presentation at the Society of Labor Economists (2016). The Synthetic Data Server (SDS) at Cornell University was set up to provide early access to new synthetic data products by the U.S. ...

Presentation given to the Society of Government Economists in May 2016.

A workshop that brought together university-based researchers, members of the international official statistical community, and other interested user communities was held on July 31, 2009, the Friday before the 2009 Joint ...