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New statistical estimation tool is a"mase"ing!

Date: August 26, 2019

Statistical estimation just became easier with this new tutorial and software package


A clip art image of a yellow rope lasso with a white background.
The “lasso” is a statistical estimator that captures only the best, of many, remotely sensed variables to use in a model. This is one of seven statistical estimators covered in the new tutorial and R package.

Background

Having precise estimates of our forest characteristics is important if we want to assess the status of our forests, detect change, or monitor trends. New statistical estimators enable us to improve precision by merging forest inventory data with data from a variety of remote sensing instruments but often pose computational challenges. This new tutorial and R software package, known as mase (model-assisted survey regression estimator) makes both old and new survey estimation tools easily accessible.

Research

National Forest inventories in the United States combine expensive ground plot data with remotely-sensed information to improve precision in estimates of forest parameters. A simple post-stratified estimator is often the tool of choice because it is efficient, easy to implement nationally, and intuitive to the many users of inventory data. Because of the increased availability of remotely-sensed data with improved spatial, temporal, and thematic grains, there is a need to equip the forest inventory community with a more diverse quiver of statistical estimators. Focusing on the broad class of model-assisted estimators under the umbrella of generalized regression estimators, we provide a tutorial that steps the reader through 7 estimators including Horvitz-Thompson, ratio, post-stratification, regression, lasso, ridge, and elastic net. Using forest inventory data from Daggett county in Utah as an example, we illustrate how to construct, as well as the relative performance of, these estimators. Each estimator is made readily accessible through the new R package, mase, available on the Comprehensive R Archival Network: https://cran.r-project.org/. We provide guidelines in the form of a decision tree on when to use which estimator in forest inventory applications.

Key Findings

  • Seven model-assisted survey estimators are made readily accessible through the new R package, mase (model-assisted survey regression estimation).
  • Guidelines are provided on when to use which estimator in forest inventory applications.
  • Bootstrap variance estimators are preferred as model complexity grows.

Additional Resources

McConville, K. G.G. Moisen, T.S. Frescino. [In review.] A tutorial in model-assisted estimation with application to forest inventory.

McConville, K. S., B. Tang, G. Zhu, S. Cheung, and S. Li. 2018. Mase: Model-Assisted Survey Estimators. https://cran.r-project.org/package=mase .

 

Featured Publications

McConville, Kelly S. ; Breidt, F. Jay ; Lee, Thomas C. M. ; Moisen, Gretchen , 2017


Principal Investigators: 
Principal Investigators - External: 
Kelly McConville - Reed College and Swarthmore College