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In this study, we employ a stratified nested sample layout that was specifically designed to test distributed models, involving measurement of snow water equivalent (SWE) under forest and in clearcuts over a range of elevations and aspects.
By simulation it was shown that optimized sampling schemes can detect scale-dependent variance components with common requirements for statistical power on smaller total sample sizes than are required with commonly used spatially nested sample designs such as the balanced design.
For example, von Davier, Xu, and Carstensen (2011) introduced a general model framework and a growth-mixture MIRT model that allows for variation in latent trajectories across clusters in a nested sample.
The full dataset and nested sample comprised 357,072 and 54,602 individuals, respectively.
Correlations between TG and LDL-C, HDL-C, TC, and HbA1c (nested sample only) were observed.
We used a nested sample strategy to set-up core collection maximizing the genetic diversity with a minimum of individuals.
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Within the nested sampling algorithm, a constrained sampling step is performed.
During periods of high runoff, nested sampling indicates that nutrient fluxes scale with catchment area.
Three 50×50 m plots were sampled in nested sampling designs of varying complexities.
Data were analyzed with hierarchical linear models to account for the nested sampling design.
The original formulation of nested sampling is a strictly sequential algorithm.
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