• Title/Summary/Keyword: systematic sampling

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Efficient Estimation of Population Mean Using Centered Modified Systematic Sampling and Interpolation

  • Kim, Hyuk-Joo;Choi, Byoung-Chul
    • Communications for Statistical Applications and Methods
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    • v.9 no.1
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    • pp.175-185
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    • 2002
  • A method is proposed for efficiently estimating the mean of a population which has a linear trend. The proposed estimator is based on the centered modified systematic sampling method and the concept of interpolation. Using the expected mean square error criterion, it is shown that the proposed method is more efficient than conventional methods in most real cases.

A Study on Estimating Population Mean by Use of Interpolation and Extrapolation with Balanced Systematic Sampling

  • Kim, Hyuk-Joo
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.1
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    • pp.91-102
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    • 1999
  • A new method is developed for estimating the mean of a population which has a linear trend. The suggested estimator is based on the balanced systematic sampling method and the concept of interpolation and extrapolation. The efficiency of the proposed method is compared with that of conventional methods.

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Estimation of Population Mean Using Centered Modified Systematic Sampling and Interpolation

  • Kim, Hyuk-Joo;Choi, Byoung-Chul
    • 한국데이터정보과학회:학술대회논문집
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    • 2001.10a
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    • pp.17-24
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    • 2001
  • A method is proposed for efficiently estimating the mean of a population which has a linear trend. The proposed estimator is based on the centered modified systematic sampling method and the concept or interpolation. Using the expected mean square error criterion, it is shown that the proposed method is more efficient than conventional methods in most real cases.

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Development of a Forest Inventory System for the Sustainable Forest Management (지속가능한 산림경영에 적합한 표본조사 방법의 개발)

  • Shin, Man Yong;Han, Won Sung
    • Journal of Korean Society of Forest Science
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    • v.95 no.3
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    • pp.370-377
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    • 2006
  • This study was conducted to develop an efficient method of sampling design appropriate for the sustainable forest management. For this, data were collected in Yangpyung-Gun, Gyunggi Province based on three different sampling designs such as systematic design, systematic cluster design, and stratified cluster design. Based on evaluation statistics, the sampling designs were compared to select a sampling method fitted to sustainable forest management. It was found that the systematical cluster sampling is the most efficient sampling method in terms of feasibility for sustainable forest management. It was also recommended that the sample plots should be made as a cluster of triangle-shape. The clusters should be consisted of a main plot and three sub-plots. And the sub-plots should be arranged with a distance of 50m from the main plot in the center of cluster.

Optimal Design of the Adaptive Searching Estimation in Spatial Sampling

  • Pyong Namkung;Byun, Jong-Seok
    • Communications for Statistical Applications and Methods
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    • v.8 no.1
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    • pp.73-85
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    • 2001
  • The spatial population existing in a plane ares, such as an animal or aerial population, have certain relationships among regions which are located within a fixed distance from one selected region. We consider with the adaptive searching estimation in spatial sampling for a spatial population. The adaptive searching estimation depends on values of sample points during the survey and on the nature of the surfaces under investigation. In this paper we study the estimation by the adaptive searching in a spatial sampling for the purpose of estimating the area possessing a particular characteristic in a spatial population. From the viewpoint of adaptive searching, we empirically compare systematic sampling with stratified sampling in spatial sampling through the simulation data.

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A Comparative Case Study on Sampling Methods for Cost-Effective Forest Inventory: Focused on Random, Systematic and Line Sampling (비용 효율적 표준지 조사를 위한 표본추출방법 비교 사례연구: 임의추출법, 계통추출법, 선상추출법을 중심으로)

  • Park, Joowon;Cho, Seungwan;Kim, Dong-geun;Jung, Geonhwi;Kim, Bomi;Woo, Heesung
    • Journal of Korean Society of Forest Science
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    • v.109 no.3
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    • pp.291-299
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    • 2020
  • The purpose of this study was to propose the most cost-effective sampling method, by analyzing the cost of forest resource investigation per sampling method for the planned harvesting area of in Chunyang-myeon, Byeonghwa-gun, Gyeongsangbuk-do, Korea. For this study, three sampling methods were selected: random sampling method, systematic sampling method, and line transect method. For each method, sample size, hourly wage, number of sample points, survey time, travel time, the sample error rate of the estimated average volume, and the desired sampling error rate were used to calculate the cost of forest resource inventories. Thus, 10 sampling points were extracted for each sampling method, and the factors required for cost analysis were calculated via a field survey. As a result, the field survey cost per ha using the random sampling method was found to be have the lowest cost, regardless of the desired sampling error rate, followed by the systematic sampling method, and the line transect method.

On Centralizing the Modified Systematic Sampling Method for Populations with Linear Trends

  • Kim, Hyuk-Joo
    • Communications for Statistical Applications and Methods
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    • v.6 no.2
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    • pp.457-466
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    • 1999
  • Centered modified systematic sampling (CMSS)' was proposed by Kim(1985) for estimating the mean of a population with a linear trend. In the present paper a version of this sampling method is suggested. This version turns out to be efficient in the same degree as the original method from the viewpoint of the expected mean square error criterion. It is also shown to be quite an efficient method as compared with other existing methods. An illustrative example is given.

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A Second Type of Centered Balanced Systematic Sampling Method

  • Hyuk Joo Kim
    • Communications for Statistical Applications and Methods
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    • v.4 no.3
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    • pp.743-752
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    • 1997
  • Kim (1985) proposed the so-called "centered balanced systematic sampling" for estimating the mean of a population with a linear trend. In this paper, a version of this sampling method is proposed. It is shown that this version is as efficient as the original method from the viewpoint of the expected mean square error criterion. It is also shown to be quite an efficient method as compared with other existing methods.g methods.

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Probability Sampling to Select Polling Places in Exit Poll (출구조사를 위한 투표소 확률추출 방법)

  • Kim, Young-Won;Uhm, Yoon-Hee
    • Survey Research
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    • v.6 no.2
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    • pp.1-32
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    • 2005
  • The accuracy of exit poll mainly depends on the sampling method of voting places. For exit poll, we propose a probability sampling method of selecting voting places as an alternative to the bellwether polling place sampling. Through an empirical study based on the 2004 general election data, the efficiency of the suggested systematic sampling from ordered voting places was evaluated in terms of mean prediction error and it turns out that the proposed sampling method outperformed the bellwether polling places sampling. We also calculated the variance of estimator from the proposed sampling, and considered the sample size problem to guarantee the target precision using the design effect of the proposed sample design.

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계통표집법의 특성에 관한 연구

  • 박진우;김영원
    • Proceedings of the Korean Association for Survey Research Conference
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    • 2000.11a
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    • pp.157-168
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    • 2000
  • In this paper we point out another advantage of systematic sampling over simple random sampling, which have not yet been spelled out in the literature. After a single sample is drawn by a sampling scheme, it is important to check whether the achived sample represents the population well or not. Therefore, a sampling scheme which avoids the possibility of selecting non-preferred samples is desirable. The simulation results are given to illustrate that, in the ordered population, the possibility of selecting non-preferred sample by systematic sampling is lower than that by simple random sampling.

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