Design-consistent model-based variances with systematic sampling: a case study with the Danish national Forest inventory

TY - JOUR

T1 - Design-consistent model-based variances with systematic sampling

T2 - a case study with the Danish national Forest inventory

AU - Magnussen, Steen

AU - Nord-Larsen, Thomas

PY - 2021

Y1 - 2021

N2 - To gauge, whether variances in density estimates computed under an assumption of a simple random sampling (SRS) are inflated–when the actual design ensures a spatial balance, we obtained design-consistent, model-based estimators from simulations with replicate samples and anticipated data. Anticipated data is the sum of model-based predictions and a random residual error. Two designs are employed, they bracket the actual design in terms of plot size and number of replicate samples. Results indicate that SRS estimates of uncertainty in ratios of totals have underestimated precision by 30% whereas precision in model-assisted ratios of totals was underestimated by 14%.

AB - To gauge, whether variances in density estimates computed under an assumption of a simple random sampling (SRS) are inflated–when the actual design ensures a spatial balance, we obtained design-consistent, model-based estimators from simulations with replicate samples and anticipated data. Anticipated data is the sum of model-based predictions and a random residual error. Two designs are employed, they bracket the actual design in terms of plot size and number of replicate samples. Results indicate that SRS estimates of uncertainty in ratios of totals have underestimated precision by 30% whereas precision in model-assisted ratios of totals was underestimated by 14%.

U2 - 10.1080/03610918.2018.1547401

DO - 10.1080/03610918.2018.1547401

M3 - Journal article

SN - 0361-0918

JO - Communications in Statistics Part B: Simulation and Computation

JF - Communications in Statistics Part B: Simulation and Computation

ER -