• Proceedings of the Korean Association for Survey Research Conference
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• 2001.04a
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• pp.59-71
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• 2001

We investigate design-based variance estimation methods of homogeneous linear estimator for population total under stratified multi-stage sampling. One method is unbiasedly estimating the first stage variance and the second stage variance separately in each stratum. And another is sub-sampling method that estimating the first stage variance only by using sub-sample selected from the second stage sample so that resulting estimator is unbiased for the total variance. The first is useful when the second stage unbiased estimator is available and the second is when the second stage variance is not estimable. For each case, we proposed a form of non-negative unbiased variance estimator. We expect the proposed variance estimation methods can be effectively used for many practical surveys.

• Survey Research
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• v.2 no.1
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• pp.59-71
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• 2001

We investigate design-based variance estimation methods of homogeneous linear estimator for population total under stratified multi-stage sampling. One method is unbiasedly estimating the first stage variance and the second stage variance separately in each stratum. And another is sub-sampling method that estimating the first stage variance only by using sub-sample selected from the second stage sample so that resulting estimator is unbiased for the total variance. The first is useful when the second stage unbiased estimator is available and the second is when the second stage variance is not estimable. For each case, we proposed a form of non-negative unbiased variance estimator. We expect the proposed variance estimation methods can be effectively used for many practical surveys.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.719-729
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• 2001

Overlap Maximization is a sampling technique to reduce survey costs and costs associated with the survey. It was first studied by Keyfitz(1951). Ernst(1998) presented a remarkable procedure for maximizing the overlap when the sampling units can be selected for two identical stratified designs simultaneously, But the approach involves mimicking the behaviour of nonlinear function by linear function and so it is less direct, even though the stratification problem for the overlap corresponds directly to the linear programming problem. furthermore, it uses the controlled selection algorithm that repeatedly needs zero-restricted controlled roundings, which are solutions of capacitated transportation problems. In this paper we suggest a comparatively simple procedure to use linear programming in order to maximize the overlap. We show how this procedure can be implemented practically.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.1
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• pp.80-89
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• 2021

In Korea, marine fish landed at ports are randomly sampled on a periodic basis (e.g., daily or weekly), and body sizes (e.g., lengths and weights) of those sampled fish are measured. The motivation for our study is whether or not such measurements reflect the size distribution, especially the length distribution of fish landed (= a population), because such length measurements are key data for a length-based assessment model. The current sampling method is to sample fish landed at ports by body size group (e.g., very small, small, medium, large, very large), using the sampling weights as the number of boxes by body size group. In this study, we showed that length composition data about fish sampled by the current method did not represent the length frequency distribution of the fish landed, and suggested that an alternative sampling method should be applied of using the sampling weights as the number of fish landed by body size group. We also introduced a method for determining an appropriate sample size.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.6 no.9
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• pp.1-5
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• 1983

This study takes over the application of survey sampling theory to activity sampling and the application of activity sampling to time standard estimation. Cluster, stratified, and multistage sampling are studied in conjunction with random and systematic sampling. Estimation procedures that will maximize the information obtained per cost expended on the study and specification of the procedure to be used to estimate the accuracy of the estimates for the adopted procedure are considered. The use of multiple regression md linear programming to estimate standard element performance time from typical job lot production data is also considered.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.403-410
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• 2023

Two-Phase sampling, which was first introduced by Neyman (1938), has various applications in different forms. Variance estimation for two-phase sampling has been an important research topic because conventional variance estimators used in most softwares are not working. In this paper, we considered a variance estimation for two-phase sampling in which stratified two-stage cluster sampling designs are used in both phases. By defining a conditionally unbiased estimator of an approximate variance estimator, which is calculable when all elements in the first phase sample are observed, we propose an explicit form of variance estimator of the double expanded estimator for a two-phase sample. A small simulation study shows the proposed variance estimator has a negligible bias with small variance. The suggested variance estimator is also applicable to other linear estimators of the population total or mean if appropriate residuals are defined.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.375-382
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• 2001

The agricultural machine estimated sales survey is a survey to estimate annual sales quantities of eight major agricultural machines such as tracter, combine, etc. The purpose of this study is to design a multipurpose sample for the agricultural machine estimated sales survey. Main achievements of this study are to present an efficient stratification criterion and to suggest a reasonable estimation method by using the concept of post-stratification.

• Survey Research
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• v.5 no.2
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• pp.3-30
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• 2004

In Korea, bellwether voting places were selected for exit poll based on the past voting results. Sometimes, voting place stratification were used to improve the exit poll performance. The sampled voting places are intended to mirror the general voters of the entire electoral district. But few studies have been done as to which sampling method works better. This study compared the four sampling methods-bellwether voting place sampling method, random sampling method, stratified bellwether sampling method and systematic sampling from ordered voting places method. When we applied the four methods to the 2004 general election data, the systematic sampling from ordered voting places method outperformed the other three sampling method. Also, we found that the additional sampling of voting places over nine contribute little to the accuracy of the estimation.

• Korean Journal of Veterinary Research
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• v.47 no.1
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• pp.51-57
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• 2007

To establish a statistically reliable sampling strategy for serological surveillance of classical swinefever (CSF) in Korea, antibody test data from CSF surveillance conducted during year 2005 were analyzed.The most appropriate sampling method was determined to be stratified multi-stage random sampling strategy,in which the primary sampling unit is a pig farm and the secondary are the pigs by the strata of breedersand finishers in the selected farm. The optimum sample size was 5 to 19 including 1 to 2 breeders accordingto the number of pigs in the farm. The optimum sampling strategy demonstrated in this study was veryFindings of our study provide practical guidelines for surveillance of herd immunity level to CSF in Korea.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1047-1061
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• 2015

Stratified random sampling is a powerful sampling strategy to reduce variance of the estimators by incorporating useful auxiliary information to stratify the population. Sample allocation is the one of the important decisions in selecting a stratified random sample. There are two common methods, the proportional allocation and Neyman allocation if we could assume data collection cost for different observation units equal. Theoretically, Neyman allocation considering the size and standard deviation of each stratum, is known to be more effective than proportional allocation which incorporates only stratum size information. However, if the information on the standard deviation is inaccurate, the performance of Neyman allocation is in doubt. It has been pointed out that Neyman allocation is not suitable for multi-purpose sample survey that requires the estimation of several characteristics. In addition to sampling error, non-response error is another factor to evaluate sampling strategy that affects the statistical precision of the estimator. We propose new sample allocation methods using the available information about stratum response rates at the designing stage to improve stratified random sampling. The proposed methods are efficient when response rates differ considerably among strata. In particular, the method using population sizes and response rates improves the Neyman allocation in multi-purpose sample survey.