An estimation strategy to protect against over-estimating precision in a LiDAR-based prediction of a stand mean

Steen Magnussen
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Steen Magnussen: Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, Canada

Journal of Forest Science, 2018, vol. 64, issue 12, 497-505

Abstract: A prediction of a forest stand mean may be biased and its estimated variance seriously underestimated when a model fitted for an ensemble of stands (stratum) does not hold for a specific stand. When the sampling design cannot support a stand-level lack-of-fit analysis, an analyst may opt to seek a protection against a possibly serious over-estimation of precision in a predicted stand mean. This study propose an estimation strategy to counter this risk by an inflation of the standard model-based estimator of variance when model predictions suggest non-trivial random stand effects, a spatial distance-dependent autocorrelation in model predictions, or both. In a simulation study, the strategy performed well when it was most needed, but equally over-inflated variance in settings where less protection was appropriate.

Keywords: forest enterprise inventory; risk analysis; stand-effects; spatial autocorrelation; simulation (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnljfs:v:64:y:2018:i:12:id:120-2018-jfs

DOI: 10.17221/120/2018-JFS

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