[
Battese, G.E., R.M. Harter, and W.A. Fuller. 1988. “An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data.” Journal of the American Statistical Association 83(401): 28–36. DOI: https://doi.org/10.2307/2288915.
]Search in Google Scholar
[
Boryan, C., Z. Yang, R. Mueller, and M. Craig. 2011. “Monitoring US agriculture: the US Depart ment of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program.” Geocarto International 26(5): 341–358. DOI: https://doi.org/10.1080/10106049.2011.562309.
]Search in Google Scholar
[
Browne, W.J., and D. Draper. 2006. “A Comparison of Bayesian and Likelihood-Based Methods for Fitting Multilevel Models.” Bayesian Analysis 1(3): 473–514. DOI: https://doi.org/10.1214/06-BA117.
]Search in Google Scholar
[
Cruze, N.B., A.L. Erciulescu, B. Nandram, W.J. Barboza, and L.J. Young. 2019. “Producing Official County-Level Agricultural Estimates in the United States: Needs and Challenges.” Statistical Science 34(2): 301–316. DOI: https://doi.org/10.1214/18-STS687.
]Search in Google Scholar
[
Erciulescu, A.L., N.B. Cruze, and B. Nandram. 2019. “Model-Based County Level Crop Estimates Incorporating Auxiliary Sources of Information.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 182(1): 283–303. DOI: https://doi.org/10.1111/rssa.12390.
]Search in Google Scholar
[
Erciulescu, A.L., N.B. Cruze, and B. Nandram. 2020. “Statistical Challenges in Combining Survey and Auxiliary Data to Produce Official Statistics.” Journal of Official Statistics 36(1): 63–88. DOI: https://doi.org/10.2478/jos-2020-0004.
]Search in Google Scholar
[
Fay, R.E., and R.A. Herriot. 1979. “Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data.” Journal of the American Statistical Association 74(366a): 269–277. DOI: https://doi.org/10.2307/2286322.
]Search in Google Scholar
[
Fuller, W.A., and J.J. Goyeneche. 1998. “Estimation of The State Variance Component.” Unpublished manuscript.
]Search in Google Scholar
[
Gelman, A., J.B. Carlin, H.S. Stern, and D.B. Rubin. 2013. Bayesian Data Analysis. CRC press. DOI: https://doi.org/10.1201/b16018.
]Search in Google Scholar
[
Gelman, A., and D.B. Rubin. 1992. “Inference from Iterative Simulation Using Multiple Sequences.” Statistical science 7(4): 457–472. DOI: https://doi.org/10.1214/ss/1177011136.
]Search in Google Scholar
[
Geweke, J. 1992. “Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments.” In Bayesian Statistics 4: 169–193. DOI: https://doi.org/10.21034/sr.148.
]Search in Google Scholar
[
Good, D. 2014. “Comparing NASS and FSA Planted Acreage Data.” Farmdoc Daily 4(9). Available at: https://farmdocdaily.illinois.edu/2014/01/comparing-nass-fsa-planted-acreage-data.html (accessed January 2014).
]Search in Google Scholar
[
Meng, X.-L. 1994. “Posterior Predictive p-Values.” The Annals of Statistics 22(3): 1142–1160. DOI: https://doi.org/10.1214/aos/1176325622.
]Search in Google Scholar
[
Nandram, B., N.B. Cruze, and A.L. Erciulescu. 2022. Bayesian Small Area Models under Inequality Constraints with Benchmarking and Double Shrinkage. Research Report RDD-22-02, National Agricultural Statistics Service, USDA. Available at: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/ResearchReport_constraintmodel.pdf /(ACCESSEDE jYKY 2022).
]Search in Google Scholar
[
Nandram, B., J. Sedransk, and S.J. Smith. 1997. “Order-Restricted Bayesian Estimation of The Age Composition of A Population of Atlantic Cod.” Journal of the American Statistical Association 92(437): 33–40. DOI: https://doi.org/10.2307/2291447.
]Search in Google Scholar
[
NASEM (National Academies of Sciences, Engineering, and Medicine). 2017. Improving Crop Estimates by Integrating Multiple Data Sources. National Academies Press. DOI: https://doi.org/10.17226/24892.
]Search in Google Scholar
[
Pfeffermann, D. 2013. “New Important Developments in Small Area Estimation.” Statistical Science 28(1): 40–68. DOI: https://doi.org/10.1214/12-STS395.
]Search in Google Scholar
[
Plummer, M. 2003. “JAGS: A Program for Analysis of Bayesian Graphical Models using Gibbs Sampling.” 3rd International Workshop on Distributed Statistical Computing (DSC 2003); March 21, Vienna, Austria: 124. Avaiable at: https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf.
]Search in Google Scholar
[
Rao, J.N.K., and I. Molina. 2015. Small Area Estimation. 2015 John Wiley/Sons, Inc. DOI: https://doi.org/10.1002/9781118735855.
]Search in Google Scholar
[
Rubin, D.B. 1984. “Bayesianly Justifiable and Relevant Frequency Calculations for the Applies Statistician.” The Annals of Statistics: 1151–1172. DOI: https://doi.org/10.1214/aos/1176346785.
]Search in Google Scholar
[
Sen, D., S. Patra, and D. Dunson. 2018. “Constrained Inference Through Posterior Projections”. arXiv preprint. DOI: https://doi.org/10.48550/arxiv.1812.05741.
]Search in Google Scholar
[
Spiegelhalter, D.J., N.G. Best, B.P. Carlin, and A. van der Linde. 2002. “Bayesian Measures of Model Complexity and Fit.” Journal of the Royal Statistical Society: Series B 64(4): 583–639. DOI: https://doi.org/10.1111/1467-9868.00353.
]Search in Google Scholar
[
Torabi, M., and J.N.K. Rao. 2014. “On Small Area Estimation under A Sub-Area Level Model.” Journal of Multivariate Analysis 127: 36–55. DOI: https://doi.org/10.1016/j.jmva.2014.02.
]Search in Google Scholar
[
USDA FSA (U.S. Department of Agriculture Farm Service Agency). 2018. FSA Handbook: Acreage and Compliance Determinations. 2-CP, Revision 16. Available at: https://www.fsa.usda.gov/Internet/FSA_File/2cp16-a1.pdf (accessed April 2018).
]Search in Google Scholar
[
USDA RMA (U.S. Department of Agriculture Risk Management Agency). 2017 The Risk Management Safety Net: Market Penetration and Market Potential-Analysis of the Federal Crop Insurance Portfolio. Available at: https://www.rma.usda.gov/-/media/RMA/Publications/Market-Penetration-and-Market-Potential-2017.ashx?la=en (accessed September 2017).
]Search in Google Scholar