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Activity Number: 433 - SPEED: Applications of Advanced Statistical Techniques in Complex Survey Data Analysis: Small Area Estimation, Propensity Scores, Multilevel Models, and More
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 2:45 PM
Sponsor: Survey Research Methods Section
Abstract #332860
Title: Empirical Bayes Small Area Prediction of Sheet and Rill Erosion Using a Zero-Inflated Lognormal Model
Author(s): Xiaodan Lyu* and Emily Berg and Heike Hofmann
Companies: Iowa State Univ and Iowa State University and Iowa State University
Keywords: small area prediction; empirical bayes; zero inflated lognormal; rainfall erosion loss; RUSLE2; CEAP
Abstract:

In the Conservation Effects Assessment Project (CEAP), some of the variables are skewed right and have zeros. We proposed an empirical Bayes (EB) estimator for the Zero-inflated Log-normal model. We are also able to approximate the variance of the estimator by estimating the conditional variance. A Monte Carlo simulation is conducted to show the empirical properties of the EB predictor and its efficiency gain over the Plugin and Zero-ignored Minimum Mean Squared Error estimator. We also applied the method to the CEAP data of South Dakota, where there are about 15% zeros among the observed RUSLE2. The proposed method is applied to get the predicted population mean of rainfall-erosion losses from cropland at the county level for South Dakota. For data analysis, we overlaid soil map shapefile and Cropland Data Layer raster in R to obtain a list of cropland map units for South Dakota, which is our target population for small area prediction. A Shiny app visualizing the overlaying procedure is developed.


Authors who are presenting talks have a * after their name.

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