• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.61-73
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• 2004

Rotation sampling designs may be classified into two categories. The first type uses the same sample unit for the entire life of the survey. The second type uses the sample unit only for a fixed number of times. In both type of designs, the entire sample is partitioned into a finite number(=G) of rotation groups. This paper is generalization of the first type designs. Since the generalized design can be identified by only G rotation groups and recall level 1, we denote this rotation system as l/G rotation design. Under l/G rotation design, variance and mean squared error (MSE) of generalized composite estimator are derived, incorporating two type of biases and exponentially decaying correlation pattern. Compromising MSE's of some selected l/G designs, we investigate design efficiency, design gap effect, ans the effects of correlation and bias.

• Survey Research
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• v.5 no.1
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• pp.103-130
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• 2004

Main monthly household surveys conducted by Korea National Statistical Office are economically active population survey(EAPS) and household income and expenditure survey(HIES). Samples of these two surveys are redesigned every 5 years based on Census. This paper is about sample redesign of household survey conducted in 2002 based on 2000 Census. Main improvements of 2002 sample redesign are the introduction of rotation sampling system, the expansion of HIES survey area from urban to whole country and the foundation of basement to make small area estimation for the unemployment statistics. Also the number of sample households within a enumeration district(ED) is reduced from 24 to 20. That makes it possible to select more ED samples which provides better precision for EAPS and HIES. To select representative samples for the population, different classification index is used for each metropolitan area and provinces.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1535-1545
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• 2014

This study aims at improving the Reliability of DSRC Traffic information considering Traffic and Road Characteristics. First of all, this study analyzed the characteristics of DSRC data on urban expressway and problems of outlier data occurrence. After then, this study produced reliable traffic information by using an optimal method of the Outlier-Filtering. After Outlier-Filtering, this study performed accuracy evaluation and appropriateness check for the number of samples per confidence level. As a result, it showed that the MAPE was between 2.2% and 9.7% and RSME was between 2.2 and 7.5 which are very similar figures to the actual average traffic speed. Also, The samples of both Am peak and Pm peak periods were analyzed to be appropriate at the confidence level of 95%, and 90% within the allowable error range of 5kph.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.453-462
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• 2017

Double propensity score adjustment is an analytic solution to address bias due to incomplete matching. However, it is difficult to estimate the standard error of the estimated treatment effect when using double propensity score adjustment. In this study, we propose two bootstrap methods to estimate the standard error. The first is a simple bootstrap method that involves drawing bootstrap samples from the matched sample using the propensity score as well as estimating the standard error from the bootstrapped samples. The second is a complex bootstrap method that draws bootstrap samples first from the original sample and then applies the propensity score matching to each bootstrapped sample. We examined the performances of the two methods using simulations under various scenarios. The estimates of standard error using the complex bootstrap were closer to the empirical standard error than those using the simple bootstrap. The simple bootstrap methods tended to underestimate. In addition, the coverage rates of a 95% confidence interval using the complex bootstrap were closer to the advertised rate of 0.95. We applied the two methods to a real data example and found also that the estimate of the standard error using the simple bootstrap was smaller than that using the complex bootstrap.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.485-499
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• 2022

Controlling the total survey error including sampling error and non-sampling error is very important in sampling design. Non-sampling error caused by non-response accounts for a large proportion of the total survey error. Many studies have been conducted to handle non-response properly. Recently, a lot of non-response imputation methods using machine learning technique and traditional statistical methods have been studied and practically used. Most imputation methods assume MCAR(missing completely at random) or MAR(missing at random) and few studies have been conducted focusing on MNAR (missing not at random) or NN(non-ignorable non-response) which cause bias and reduce the accuracy of imputation. In this study, we propose a non-response imputation method that can be applied to non-ignorable non-response. That is, we propose an imputation method to improve the accuracy of estimation by removing the bias caused by NN. In addition, the superiority of the proposed method is confirmed through small simulation studies.

• Survey Research
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• v.5 no.2
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• pp.31-48
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• 2004

We collected and analyzed 18 social surveys to assess the quality of samples with respect to region, gender, age-band, education level and occupation. We found in our samples that highly educated people and house wives are over-represented whereas low educated people, self-employed/blue collars and white collars are under-represented. To correct such sample distortions, we applied the iterative proportional weighting or the raking to our samples. We observed sizable changes in survey results. Also, the effective sample sizes were shrunken up to 20%-40%, that could be interpreted as the necessity of larger samples to meet the claimed sampling error limits.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.26 no.4
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• pp.293-297
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• 1981

An attempt has been made to determine the optimum size of sampling unit and the number of samples for a given precision in wheat, using the data collected from the various experiments in 1979/80. It was found that the coefficients of variation for number of spikes except the case of high-ridge broadcasting by 8HP rotarized seeder are in the same order of those for yield of wheat, and the regression coefficients associated with the coefficients of variation and the size of sampling unit were significant at 1% level of type I error. A wide range of variation in the size of sampling unit was observed for different methods of seeding, indicating the proper sizes of sampling units for 40cm \times 18cm, 60cm \times 18cm, 20cm \times 5cm, 120cm \times 90cm to be 0.40$m^2$, 0.17$m^2$, , 0.11$m^2$, , 0.55$m^2$, , respectively. The variance component for the experimental error was not physically possible to estimate due probably to high variability among the sampling units. The number of the sampling units per plot for a given precision of CV=12% was estimated to be one in an experiment with 4 replicates.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.2
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• pp.67-73
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• 1985

In Digital Terrain Model(DTM), more accurate data acquisition method is of importance. This paper has the purpose of accuracy analysis of progressive sampling method, one of data acquisition method. Especially, The following in accuracy analysis are compared and analyzed. -Comparison and analysis for position error between the digital contour lines using digital terrain model and the conventional contour lines using A-10 Plotter. -Analysis for height error of interpolation points according to application of progressive sampling method. For above numerical tests, Computer Program related to auto-carto of contour lines was made up. As a result of tests, threshold and sampling criterion have close of mutual relation to accuracy. Particularly, it was found that auto-carto of contour lines-threshold of 1.0 m and standard criterion-almost concurred in conventional contour lines.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.923-932
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• 2010

One of the problems in designing the fifth Korean National Health and Nutrition Examination Survey(KNHNES) is the lack of an appropriate sampling frame. Due to the significant time difference, we expect eight severe sampling frame errors if we use the sampling frame obtained from the latest population and housing census that was conducted in 2005. Thus, the construction of an appropriate sampling frame for the fifth KNHNES is crucial for a successful survey. We considered the construction of a sampling frame that overcomes the limitations of the 2005 population and housing census based frame. For the construction of eight new sampling frames, we considered the use of multiple sampling frames in which the frame for the apartment households and the frame for the general households are obtained from different sources.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1033-1041
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• 2013

In this paper, we investigate several variance estimators for pps systematic sampling. Unfortunately, there is no unbiased variance estimators for a systematic sample because systematic sampling can be regarded as a random selection of one cluster. This study provides guidance on which variance estimator may be more appropriate than others in several circumstances. We judge the efficiency of variance estimators for systematic sampling based on of their relative biases and relative mean square error. Also, we investigate variance estimation problems for two-stage systematic sampling applied for the Food Raw Material Consumption Survey and the Establishment Labor Force Survey simulation study, in order to consider the popular two-stage pps systematic sample design for establishment and household survey in Korea.