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Follow on Google News | NISS and NASS Collaboration Kicks Off June 13 teams from the National Institute of Statistical Sciences & the National Agricultural Statistics Services, and faculty and graduate students from 5 universities will convene at NISS in RTP on June 1-2 to kick off the 2-year research program.
Team one will address, “Multivariate Imputation of Agricultural Resource Management Survey Data” for implementation in Agricultural Resource Management Survey – Phase III. Faculty mentors for this team will be Professors Sujit K. Ghosh from the Statistics Department of North Carolina State University (NCSU) and Barry Goodwin, William Neal Reynolds Distinguished Professor, Departments of Agricultural and Resource Economics and Economics at NCSU. Darcy Miller, Tim Keller, and Peter Quan will provide leadership from NASS; and Kirk White from the USDA Economic Research Service will also join the team. Michael Robbins from Clemson University will spend two years as a NISS-NASS Postdoctoral Fellow working on this research; and Joshua Habiger, a graduate student at the University of South Carolina will complete this team. Team two will focus on “Design and Estimation Methodologies for Estimating the Number of Small Farms from NASS Sampling Frames.” Faculty mentors will be Linda Young, Professor at the University of Florida Statistics Department and Pam Arroway, Associate Professor at North Carolina State University. The NASS project leaders will be Andrea Lamas, and Denise Abreu. Patricia Gunning, NISS-NASS Postdoctoral Fellow, received her doctorate from Dublin City University; and Kenny Lopiano is a graduate student at the University of Florida. Team three will research “Statistical Multi-source Predictive Models and Error Estimation in Support of Crop Production Forecasts and Estimates.” Professor Balgobin Nandram, of the Mathematical Sciences Department at Worcester Polytechnic Institute will be the senior faculty mentor; he will be joined by Scott Holan Assistant Professor at the University of Missouri, Department of Statistics. The NASS project leads are Wendy Barboza and Edwin Anderson. Jianqiang (Jay) Wang will join the team as the postdoctoral fellow. After receiving his Ph.D. in Statistics from Iowa State University, Jay spent a year at Colorado State University as a Visiting Assistant Professor. Criselda Toto who is a doctoral candidate in Statistics at Worcester Polytechnic Institute will also join this team. The three teams will be in residence at NISS during the summers of 2009 and 2010. Postdoctoral fellows will spend the remainder of their two-year appointments at NASS in the Washington DC area working directly with NASS project leaders, while faculty and graduate students will return to their universities during the academic years. About NISS The National Institute of Statistical Sciences was established in 1990 by the national statistics societies and the Research Triangle universities and organizations, with the mission to identify, catalyze and foster high-impact, cross-disciplinary and cross-sector research involving the statistical sciences. NISS is dedicated to strengthening and serving the national statistics community, most notably by catalyzing community members’ participation in applied research driven by challenges facing government and industry. NISS also provides career development opportunities for statisticians and scientists, especially those in the formative stages of their careers. NISS is located in Research Triangle Park, North Carolina. For more information about the NISS, go to www.niss.org. About USDA/NASS The National Agricultural Statistics Service (NASS) is the survey and estimation arm of the U.S. Department of Agriculture. NASS conducts hundreds of surveys each year and conducts a Census of Agriculture every five years. These national surveys, in combination with various sources of available administrative data, form a rich data basis for forecasting/ # # # NISS was established in 1991 by the national statistics societies and the Research Triangle universities and organizations, with the mission to identify, catalyze and foster high-impact, cross- disciplinary research involving the statistical sciences. End
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