The National Institute of Statistical Sciences (NISS), a private, non-profit organization focusing on statistical research, has established a Cross-Sector Research in Residence Program in partnership with the National Agricultural Statistics Service (NASS), the survey and estimation arm of the U.S. Department of Agriculture. This new collaborative venture by NISS and the USDA is the first project of a NISS initiative to host academic-government research teams focused on specific federal agency objectives.
Each team of five people will comprise a faculty researcher in statistics, a NASS researcher, a NISS mentor, a postdoctoral fellow and a graduate student who will work intensively together at NISS during the summers of 2009 and 2010 to solve research questions posed by NASS.
Varied projects will focus on advances in statistical methodology for implementation in USDA surveys and analysis of survey results. Four candidate areas of research have already been identified; and applications to work on each of these are now being sought from university faculty and students.
- Multivariate Imputation Mechanisms and Valid Mean Squared Error Estimation: Agricultural Resource Management Survey – Phase III
One of the objectives of the Agricultural Resource Management Survey – Phase III is to allow statisticians and economists to conduct multivariate statistical analyses of the farm economy with valid estimates for the potential error in model estimates and forecasts. NASS has been using a univariate approach to both imputation and mean-squared-error estimates, but multivariate approaches are needed to support multiple estimates and simultaneous forecasts for multiple crops. Development of a multiple-imputation scheme will have to handle the complexities associated with heterogeneous data, and also the semi-continuous nature of agricultural data. The second challenge is to determine the validity of the method when the prediction models underlying its imputation fail.
- New Design and Estimation Methodologies for Biased Self-Exclusion (Under-coverage): Estimation of Small Farms from Census Mail List NASS accounts for the incompleteness of its Census Mail List (CML)
Census respondents to capture the estimated number of farms identified on the area-frame, but not on the CMLt. When the 2007 Census was processed, NASS also identified several valid farms that were not found in the areaframe, even though they were located in sampled area segments. This poses the question of how many farms are missed by both sources, CML and area-frame. The challenge here is to develop statistical procedures to measure the number of farms missing from both frames and to incorporate these into Census weights. Cognitive issues may also need to be addressed since many qualifying small farms do not necessarily consider themselves farms and hence fail to return the survey forms.
- New Statistical Editing and Imputation Methods That Preserve Data Quality: Quarterly Agricultural Survey
NASS utilizes data cleaning/editing procedures in many of its surveys that are based on an expert opinion/analysis review process and manual intervention to correct identified data values outside of normally expected ranges. This manual editing process is time consuming and is not consistent. It can lead to edit effects that are not reflected in the measurement error process. The objective here is to create automated statistical/selective editing and imputation strategies that could reduce the non sampling errors and lower the survey cost by reducing the extensive staff resources currently used in the data cleaning process.
- Statistical Multi-Source Predictive Models and Error Estimates: Major USDA Crop Protection Forecasts and Estimates
The USDA produces multiple forecasts of crop protection throughout the growing season and estimates production at the end-of-season or after harvest. Information is collected from multiple sources (USDA surveys and administrative/auxiliary information, including weather and remotely sensed data) and then synthesized by a panel of experts in USDA's Agricultural Statistics Board (ASB) resulting in the official forecasts/estimates that are published. These forecasts are compared to the utilization of the crops and assessed for accuracy. Subsequently, when the actual yields are known, can improvements be made to this process via increased use of data modeling or through other approaches? How can these models or other techniques be validated during the short time period analysts have to review the inputs and publish the time sensitive official estimates?
The research teams will examine these focus areas over two consecutive summer periods. The program will start in the summer of 2009 when the complete teams meet at NISS; during the first three weeks, each team will formulate its research agenda. The postdoctoral fellow and graduate student will spend the summer at NISS working on the project under NISS mentorship, with periodic meetings with the faculty member and the NASS researcher. During the academic year, the postdoctoral fellow will reside at the USDA, to continue to work with the NASS researcher. In the summer of 2010, the teams will meet again at NISS and complete their work.
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/estimating a broad array of farm and rural characteristics covering virtually every aspect of U.S. agriculture. Production and supplies of food and fiber, prices paid and received by farmers, farm labor and wages, farm finances, chemical use, and changes in the demographics of U.S. producers are some examples of the statistical data gathered by NASS.
About NISS
The National Institute of Statistical Sciences was established in 1990 by the national statistics societies and the Research Triangle universities and organizations. Its mission is 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 engaging community members 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/NASS Cross-Sector Research in Residence Program, or to apply for one of the positions, go to http://www.niss.org/rir200910/Summer-Research-in-Residence2009-10.html.