• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.141-148
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• 2004

우리는 모분산 $\sigma^2$에 대한 추정량으로서 표본분(equation omitted)을 주로 사용한다. 그러나, 제 7차 교육 과정에 따른 고등학교 수학 교과서(10-가, 수학 I과 실용수학)에서는 표본분산의 정의를(equation omitted)으로 사용하고 있다. 이 두 표본분산들의 관계를 알아보고, 시뮬레이션을 통하여 확인하여 본다.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.689-699
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• 2005

We usually use $S^2=\frac{{\Sigma}^n_{i=1}(X_i-\={X})^2}{n-1}$ as sample variance. Korean high school text-books use $S^2_n=\frac{{\Sigma}^n_{i=1}(X_i-\={X})^2}{n}$as sample variance. We can compare the above two definitions of sample variance through their theoretical relationship and simulation.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.127-132
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• 2002

절단된 정규분포의 평균과 분산을 추정하기 위하여 전체 표본에 기초한 최대가능도 추정량을 사용한 방법과 절단된 후에 남아있는 표본만을 고려한 절단된 표본의 표본평균과 표본분산을 시뮬레이션을 통해 비교 연구하였다. 평균을 추정하는 경우에는 놀랍게도 절단된 자료에 기초한 추정량이 전체 표본에 기초한 추정량보다 평균제곱오차가 더 작다는 것을 발견하였다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.981-999
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• 2017

In the standard control chart assuming a simple random model, we estimate the process variance without considering the between-sample variance. If the between-sample exists in the process, the process variance is under-estimated. When the process variance is under-estimated, the narrower control limits result in the excessive false alarm rate although the sensitivity of the control chart is improved. In this paper, using the variance component model to incorporate the between-sample variance, we set the control limits using both the within- and between-sample variances, and evaluate the efficiency of the control chart in terms of the average run length (ARL). Considering the most widely used control chart types such as ${\bar{X}}$, EWMA and CUSUM control charts, we compared the differences between two cases, Case I and Case II, where the between-sample variance is ignored and considered, respectively. We also considered the two cases when the process parameters are given and estimated. The results showed that the false alarm rate of Case I increased sharply as the between-sample variance increases, while that of Case II remains the same regardless of the size of the between-sample variance, as expected.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.177-182
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• 2008

There are times when we need more sample to achieve a more accurate estimator. Since these two sets of sample have the information about the same population, it is necessary to treat both as a single combined data. In this paper we present the unpooled sample variance for the combined data when we just know a sample mean and variance for the each data set without the raw data. It is shown that the pooled variance $s^2_p$ is always greater than the exact variance $s^2_t$ when ${\bar{x}}_n\;=\;{\bar{y}}_m$. And the difference of means for two data, ${\bar{x}}_n-{\bar{y}}_m}$, is larger, the difference of $s^2_p$ and $s^2_t$ is larger.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.39-43
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• 2000

분산 추정 및 신뢰구간 추정의 한 방법으로 널리 쓰이고 있는 붓스트랩 방법을 복합표본에 적용하는 방법에 대해 알아보았다. 복합 표본은 유한 모집단에서 추출되고 추출확률이 다르기 때문에 i.i.d. 표본에 기초하여 개발된 전통적인 붓스트랩 방법을 직접 적용하면 추론의 오류가 발생할 수 있다. 본 연구에서는 복원 확률비례표본과 랜덤그룹표본에 붓스트랩을 적용하는 방법을 알아보았다.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.131-135
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• 2003

표본조사를 하는 경우에 사전에 전체 표본의 크기를 정하여 놓고, 표본설계를 하는 경우가 많다. 이 때에는 조사 비용은 고려의 대상이 안되고 주어진 전체표본 크기로 각 층별로 표본을 할당하여 분산을 최소로 하는 문제가 된다. 이 논문에서는 pps 집락추출과 각 집락에서 같은 크기의 부표본(subsample)을 추출하여 자체 가중이 되도록 표본설계를 하는 경우에 표본의 크기 $m_{0}$가 사전에 주어졌을 때에 모총계의 추정량의 분산을 최소로 하는 최적의 표본추출율을 구하고. 이러한 $m_{0}$값들 중에서 최적의 $m_{opt}$값을 구한다.

• 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.

• 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.

• Korean journal of applied entomology
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• v.51 no.2
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• pp.91-97
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• 2012

The dispersion indices, spatial pattern and sampling plan for pink citrus rust mite (PCRM), Aculops pelekassi, monitoring was investigated. Dispersion indices of PCRM indicated the aggregated spatial pattern. Taylor's power law provided better description of variance-mean relationship than Iwao's patchiness regression. Fixed-precision levels (D) of a sequential sampling plan were developed using by Taylor's power law parameters generated from PCRM on fruit sample (cumulated number of PCRM in $cm^2$ of fruit). Based on Kono-Sugino's empirical binomial the mean density per $cm^2$ could be estimated from fruit ratio with more than 12 rust mites per $cm^2$: $ln(m)=4.61+1.23ln[-ln(1-p_{12})]$. To determine the optimal tally threshold, the variance (var(lnm)) for mean (lnm) in Kono-Sugino equation was estimated. The lower and narrow ranged change of variance for esimated mean showed at a tally threshold of 12. To estimate PCRM mean density per $cm^2$ at fixed precision level 0.25, the required sample number was 13 trees, 5 fruits per tree and 2 points per fruit (total 130 samples).