• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.637-644
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• 2001

We investigated two kinds of mean square error (MSE) estimator of homogeneous linear estimator (HLE) for the population total under stratified multi-stage sampling. One is studied when the second stage variance component is estimable and the other is found in cafe it is not estimable. The proposed estimators are necessary forms of non-negative unbiased MSE estimators of HLE.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.191-196
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• 2005

Knowing the time of the process change could lead to quicker identification of the responsible special cause and less process down time, and it could help to reduce the probability of incorrectly identifying the special cause. In this paper, we propose a MLE of the process change point when control charts with the fixed sampling rate (FSR) scheme or the variable sampling rate (VSR) scheme monitor a process to detect changes in the process mean and/or variance of a normal quality variable.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.101-106
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• 1994

The basic of the VSI charts is that if the sample statistic computed after a sample is taken shows some indication of a process change than the sampling interval before the next sample should be short otherwise long. This paper was shown the VSS chart can be considerably more efficient than the FSS chart and the effectiveness of VSI R chart with S chart used for monitoring a process standard deviation.

• Journal of the Korean Statistical Society
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• v.35 no.4
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• pp.441-452
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• 2006

Here an efficient regression type estimator for a stratified population mean is proposed under the two-phase sampling scheme. While constructing the proposed estimator, it is assumed that the first auxiliary variable x is directly and highly correlated with the study variable y, and the second auxiliary variable z is directly and highly correlated with the first auxiliary variable x. However the variable z is not directly correlated with the variable y, but they are just correlated with each other only due to their direct and high correlation with the variable x. The proposed regression type estimator is found to be always more efficient than the existing estimators defined under the same situation.

• Proceedings of the Korean Association for Survey Research Conference
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• 2006.12a
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• pp.67-83
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• 2006

In successive sampling on two occasions the problem of estimating a finite population quantile has been considered. The theory developed aims at providing the optimum estimates by combining (i) three double sampling estimators viz. ratio-type, product-type and regression-type, from the matched portion of the sample and (ii) a simple quantile based on a random sample from the unmatched portion of the sample on the second occasion. The approximate variance formulae of the suggested estimators have been obtained. Optimal matching fraction is discussed. A simulation study is carried out in order to compare the three estimators and direct estimator. It is found that the performance of the regression-type estimator is the best among all the estimators discussed here.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.327-347
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• 2020

We consider a credit portfolio with highly skewed exposures. In the portfolio, small number of obligors have very high exposures compared to the others. For the Bernoulli mixture model with highly skewed exposures, we propose a new importance sampling scheme to estimate the tail loss probability over a threshold and the corresponding expected shortfall. We stratify the sample space of the default events into two subsets. One consists of the events that the obligors with heavy exposures default simultaneously. We expect that typical tail loss events belong to the set. In our proposed scheme, the tail loss probability and the expected shortfall corresponding to this type of events are estimated by a conditional Monte Carlo, which results in variance reduction. We analyze the properties of the proposed scheme mathematically. In numerical study, the performance of the proposed scheme is compared with an existing importance sampling method.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.565-576
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• 2002

We propose a new sampling design for the 2001 Health Index Survey at Seoul. In this stratified two-stage sampling design, the ED(enumeration district) of 2000 Population and Housing Census is used as primary sampling unit and the Gu is used as stratification variable in order to obtain the sub-domain estimate for 25 Gu's as well as population estimate for Seoul. The sample ED's are systematically selected after the Ed's are ordered by location and property to obtain a representative sample. And also, the imputation methods for item nonresponses are suggested.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.543-556
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• 2007

In successive sampling on two occasions the problem of estimating a finite population quantile has been considered. The theory developed aims at providing the optimum estimates by combining (i) three double sampling estimators viz. ratio-type, product-type and regression-type, from the matched portion of the sample and (ii) a simple quantile based on a random sample from the unmatched portion of the sample on the second occasion. The approximate variance formulae of the suggested estimators have been obtained. Optimal matching fraction is discussed. A simulation study is carried out in order to compare the three estimators and direct estimator. It is found that the performance of the regression-type estimator is the best among all the estimators discussed here.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.70-79
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• 1999

Importance Sampling (IS) has been applied to accelerate the occurrence of rare events. However, it has a drawback of effective biasing scheme to make the estimator from IS unbiased. Adaptive Importance Sampling (AIS) employs an estimated sampling distribution of IS to the systems of interest during the course of simulation. We propose Nonparametric Adaptive Importance Sampling (NAIS) technique which is nonparametrically modified version of AIS and test it to estimate a probability of rare event in M/M/1 queueing model. Comparing with classical Monte Carlo simulation, the computational efficiency and variance reductions gained via NAIS are substantial. A possible extension of NAIS regarding with random number generation is also discussed.

• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.133-141
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• 2009

This study was conducted to support for determining an efficient sampling design for forest resources assessments in South Korea with respect to statistical efficiency. For this objective, different systematic sampling designs were simulated and compared based on an artificial forest population that had been built from field sample data and satellite data in Yang-Pyeong County, Korea. Using the k-NN technique, two thematic maps (growing stock and forest cover type per pixel unit) across the test area were generated; field data (n=191) and Landsat ETM+ were used as source data. Four sampling designs (systematic sampling, systematic sampling for post-stratification, systematic cluster sampling, and stratified systematic sampling) were employed as optimum sampling design candidates. In order to compute error variance, the Monte Carlo simulation was used (k=1,000). Then, sampling error and relative efficiency were compared. When the objective of an inventory was to obtain estimations for the entire population, systematic cluster sampling was superior to the other sampling designs. If its objective is to obtain estimations for each sub-population, post-stratification gave a better estimation. In order to successfully perform this procedure, it requires clear definitions of strata of interest per field observation unit for efficient stratification.