• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.353-369
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• 2000

We systematized an stratified spatial sample design(SSSD) that uses the adequate stratification criteria such as the shapeness or the dispersion of an interesting region in a spatial population. And we proposed an adaptive searching estimation method in the SSSD to estimate the area of region of interest in two-dimensional surfaces. When wc adopt the proposed adaptive searching estimation method in SSSD, the observing sample size is more decreased than a classical sample design that all the designed sample size is observed. Nevertheless it has been shown that we can produce the moderate result but the efficiency is a slight reduced.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.9-16
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• 2010

The sampling design for the spatial population studies needs a model assumption of a dependent relationship, where the interesting parameters can be the population mean, proportion and area. We know that the study of an interested spatial population, which is stratified by a geographical condition or shape, and the degree of distort of an estimation area is much useful. In light of this, if auxiliary information of the target variable such as wasted area contaminated by some material and the degree of distribution of animal or plants is available, then the spatial estimator might be improved through the calibration procedure. In this research, we propose the calibration procedure for the spatial stratified sampling in which we consider the one and two-dimensional auxiliary information.

• Journal of Korean Society of Forest Science
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• v.99 no.3
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• pp.285-291
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• 2010

Since 2006, the $5^{th}$ National Forest Inventory (NFI) has been implemented to provide forest resources statistics at the national level and at the county level as well. However, it needs a small-area estimator for estimating forest statistics at the county-level due to a small number of samples collected within a county. This study was conducted to evaluate the applicability of a geographical-based synthetic estimator for estimating forest growing stock volumes at the county level. The NFI-field plots surveyed were post-stratified into three forest cover types. In the synthetic estimator, field plots within a geographical-based super-county for each county were used to estimate stratum weights and stratum mean volumes. It was resulted that estimated stratum weights using the synthetic estimation were significantly differ from forest cover maps. The standard errors of estimated mean by the synthetic estimation that ranged from ${\pm}3.5\;m^3$/ha to ${\pm}7.7\;m^3$/ha were more smaller than those (${\pm}7.8\;m^3/ha{\sim}{\pm}24.7\;m^3/ha$) by the direct estimation. This means that the synthetic estimation is possible to provide more precise estimates of mean volumes.