TY  - RPRT
T1  - Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Secure the Future of the Federal Statistical System?
Y1  - 2017
A1  - Weinberg, Daniel
A1  - Abowd, John M.
A1  - Belli, Robert F.
A1  - Cressie, Noel
A1  - Folch, David C.
A1  - Holan, Scott H.
A1  - Levenstein, Margaret C.
A1  - Olson, Kristen M.
A1  - Reiter, Jerome P.
A1  - Shapiro, Matthew D.
A1  - Smyth, Jolene
A1  - Soh, Leen-Kiat
A1  - Spencer, Bruce
A1  - Spielman, Seth E.
A1  - Vilhuber, Lars
A1  - Wikle, Christopher
AB  - <p>Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Secure the Future of the Federal Statistical System? Weinberg, Daniel; Abowd, John M.; Belli, Robert F.; Cressie, Noel; Folch, David C.; Holan, Scott H.; Levenstein, Margaret C.; Olson, Kristen M.; Reiter, Jerome P.; Shapiro, Matthew D.; Smyth, Jolene; Soh, Leen-Kiat; Spencer, Bruce; Spielman, Seth E.; Vilhuber, Lars; Wikle, Christopher The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This paper discusses some of the key research findings of the eight nodes, organized into six topics: (1) Improving census and survey data collection methods; (2) Using alternative sources of data; (3) Protecting privacy and confidentiality by improving disclosure avoidance; (4) Using spatial and spatio-temporal statistical modeling to improve estimates; (5) Assessing data cost and quality tradeoffs; and (6) Combining information from multiple sources. It also reports on collaborations across nodes and with federal agencies, new software developed, and educational activities and outcomes. The paper concludes with an evaluation of the ability of the FSS to apply the NCRN’s research outcomes and suggests some next steps, as well as the implications of this research-network model for future federal government renewal initiatives. This paper began as a May 8, 2015 presentation to the National Academies of Science’s Committee on National Statistics by two of the principal investigators of the National Science Foundation-Census Bureau Research Network (NCRN) – John Abowd and the late Steve Fienberg (Carnegie Mellon University). The authors acknowledge the contributions of the other principal investigators of the NCRN who are not co-authors of the paper (William Block, William Eddy, Alan Karr, Charles Manski, Nicholas Nagle, and Rebecca Nugent), the co- principal investigators, and the comments of Patrick Cantwell, Constance Citro, Adam Eck, Brian Harris-Kojetin, and Eloise Parker. We note with sorrow the deaths of Stephen Fienberg and Allan McCutcheon, two of the original NCRN principal investigators. The principal investigators also wish to acknowledge Cheryl Eavey’s sterling grant administration on behalf of the NSF. The conclusions reached in this paper are not the responsibility of the National Science Foundation (NSF), the Census Bureau, or any of the institutions to which the authors belong</p>
PB  - NCRN Coordinating Office
UR  - http://hdl.handle.net/1813/52650
ER  - 

TY  - RPRT
T1  - Using Social Media to Measure Labor Market Flows
Y1  - 2014
A1  - Antenucci, Dolan
A1  - Cafarella, Michael J
A1  - Levenstein, Margaret C.
A1  - Ré, Christopher
A1  - Shapiro, Matthew
UR  - http://www-personal.umich.edu/~shapiro/papers/LaborFlowsSocialMedia.pdf
ER  - 

TY  - JOUR
T1  - Ringtail: Feature Selection for Easier Nowcasting.
JF  - WebDB
Y1  - 2013
A1  - Antenucci, Dolan
A1  - Cafarella, Michael J
A1  - Levenstein, Margaret C.
A1  - Ré, Christopher
A1  - Shapiro, Matthew
AB  - In recent years, social media “nowcasting”—the use of on- line user activity to predict various ongoing real-world social phenomena—has become a popular research topic; yet, this popularity has not led to widespread actual practice. We be- lieve a major obstacle to widespread adoption is the feature selection problem. Typical nowcasting systems require the user to choose a set of relevant social media objects, which is difficult, time-consuming, and can imply a statistical back- ground that users may not have.  We propose Ringtail, which helps the user choose rele- vant social media signals. It takes a single user input string (e.g., unemployment) and yields a number of relevant signals the user can use to build a nowcasting model. We evaluate Ringtail on six different topics using a corpus of almost 6 billion tweets, showing that features chosen by Ringtail in a wholly-automated way are better or as good as those from a human and substantially better if Ringtail receives some human assistance. In all cases, Ringtail reduces the burden on the user.
UR  - http://www.cs.stanford.edu/people/chrismre/papers/webdb_ringtail.pdf
ER  -