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Abstract
An example of endogenous post-stratification is the use of remote sensing data with a sample of ground data to build a logistic regression model to predict the probability that a plot is forested and using the predicted probabilities to form categories for post-stratification. An optimized endogenous post-stratified estimator of the proportion of forest has been recently proposed in the literature, but there are no known literature results describing the operating characteristics of this estimator. This study reports the results of a detailed Monte Carlo investigation of the performance of the optimized and another endogenous post-stratified estimator under a variety of realistic scenarios and compares their performance with earlier approaches.
Parent Publication
Citation
Patterson, Paul L. 2012. Optimized endogenous post-stratification in forest inventories. In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 342-347.
