14. Small Area Estimation: Uses in Agricultural Experiments

In agricultural, field-based experiments it is common to take multiple subsamples of the response variable within a given experimental unit. This method is commonly deployed due to variability in observations within experimental units. These subsamples are often averaged together prior to analysis so that data analysis can be performed on the experimental unit level. Given averaging reduces the associated variance, we explore the impact of this practice on the probability of making a type I error in varied simulated settings. Small area estimation is a category of techniques that can be used to provide more accurate estimates within a small area or domain. Model based small area estimates combine direct sample data with auxiliary data. These estimates generally have lower mean squared error than the direct sample data alone, and avoid the need for researchers to average subsample observations within plots. Small area estimation research has been widely used within survey applications but little work has been done in the context of designed experiments. This poster will explore potential benefits of using small area estimation models in designed experiments where multiple subsamples are taken. Ultimately, guidance will help practitioners in designing experiments to leverage the benefits of small area models and techniques will be demonstrated in a real data analysis based on simulated settings.

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Women in Statistics and Data Science 2024