• Title/Summary/Keyword: Sampling variance

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Design-based Variance Estimation under stratified Multi-stage Sampling (층화 다단계 샘플링에서 설계 기반 분산추정)

  • 김규성
    • 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.

Design-based Variance Estimation under Stratified Multi-stage Sampling (층화 다단계 샘플링에서 설계 기반 분산추정)

  • 김규성
    • 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.

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Effect Analysis of Sample Size and Sampling Periods on Accuracy of Reliability Estimation Methods for One-shot Systems using Multiple Comparisons (다중비교를 이용한 샘플수와 샘플링 시점수의 원샷 시스템 신뢰도 추정방법 정확성에 대한 영향 분석)

  • Son, Young-Kap
    • Journal of the Korea Institute of Military Science and Technology
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    • v.15 no.4
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    • pp.435-441
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    • 2012
  • This paper provides simulation-based results of effect analysis of sample size and sampling periods on accuracy of reliability estimation methods using multiple comparisons with analysis of variance. Sum of squared errors in estimated reliability measures were evaluated through applying seven estimation methods for one-shot systems to simulated quantal-response data. Analysis of variance was implemented to investigate change in these errors according to variations of sample size and sampling periods for each estimation method, and then the effect analysis on accuracy in reliability estimation was performed using multiple comparisons based on sample size and sampling periods. An efficient way to allocate both sample size and sampling periods for reliability estimation tests of one-shot systems is proposed in this paper from the effect analysis results.

An Optimal Scheme of Inclusion Probability Proportional to Size Sampling

  • Kim Sun Woong
    • Communications for Statistical Applications and Methods
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    • v.12 no.1
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    • pp.181-189
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    • 2005
  • This paper suggest a method of inclusion probability proportional to size sampling that provides a non-negative and stable variance estimator. The sampling procedure is quite simple and flexible since a sampling design is easily obtained using mathematical programming. This scheme appears to be preferable to Nigam, Kumar and Gupta's (1984) method which uses a balanced incomplete block designs. A comparison is made with their method through an example in the literature.

Linear Measurement Error Variance Estimation based on the Complex Sample Survey Data

  • Heo, Sunyeong;Chang, Duk-Joon
    • Journal of Integrative Natural Science
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    • v.5 no.3
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    • pp.157-162
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    • 2012
  • Measurement error is one of main source of error in survey. It is generally defined as the difference between an observed value and an underlying true value. An observed value with error may be expressed as a function of the true value plus error term. In some cases, the measurement error variance may be also a function of the unknown true value. The error variance function can be rewritten as a function of true value multiplied by a scale factor. This research explore methods for estimation of the measurement error variance based on the data from complex sampling design. We consider the case in which the variance of mesurement error is a linear function of unknown true value, and the error variance scale factor is small. We applied our results to the U.S. Third National Health and Nutrition Examination Survey (the U.S. NHANES III) data for empirical analyses, which has replicate measurements for relatively small subset of initial respondents's group.

A Note on Complex Two-Phase Sampling with Different Sampling Units of Each Phase

  • Lee, Sang Eun;Jin, Young;Shin, Key-Il
    • Communications for Statistical Applications and Methods
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    • v.22 no.5
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    • pp.435-443
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    • 2015
  • Two phase sampling design is useful to increase estimation efficiency using deep stratification, improved non-response adjustment and reduced coverage bias. The same sampling units are commonly used for the first and the second phases in complex two-phase sampling design. In this paper we consider a sampling scheme where the first phase sampling units are clusters and the second phase sampling units are list samples. Using selected clusters in first phase requires that we list up elements in the selected clusters from the first phase and then use the list as a secondary sampling frame for the second phase sampling design. Then we select second phase samples from the listed sampling frame. We suggest an estimator based on the complex two-phase sampling design with different sampling units of each phase. Also the estimated variances of the estimator obtained by using classic and replication variance methods are considered and compared using simulation studies. For real data analysis, 2010 Korea Farm Household Economy Survey (KFHES) and 2011 Korea Agriculture Survey (KAS) are used.

EFFICIENT REPLICATION VARIANCE ESTIMATION FOR TWO-PHASE SAMPLING

  • Kim, Jae-Kwang;Sitter, Randy
    • Proceedings of the Korean Statistical Society Conference
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    • 2002.11a
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    • pp.327-332
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    • 2002
  • Variance estimation for the regression estimator for a two-phase sample is investigated. A replication variance estimator with number of replicates equal to or slightly larger than the size of the second-phase sample is developed. In these cases, the proposed method is asymptotically equivalent to the full jackknife, but uses smaller number of replications.

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Variable sampling interval control charts for variance-covariance matrix

  • Chang, Duk-Joon;Shin, Jae-Kyoung
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.4
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    • pp.741-747
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    • 2009
  • Properties of multivariate Shewhart and EWMA (Exponentially Weighted Moving Average) control charts for monitoring variance-covariance matrix of quality variables are investigated. Performances of the proposed charts are evaluated for matched fixed sampling interval (FSI) and variable sampling interval (VSI) charts in terms of average time to signal (ATS) and average number of samples to signal (ANSS). Average number of swiches (ANSW) of the proposed VSI charts are also investigated.

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Asymptotics for realized covariance under market microstructure noise and sampling frequency determination

  • Shin, Dong Wan;Hwang, Eunju
    • Communications for Statistical Applications and Methods
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    • v.23 no.5
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    • pp.411-421
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    • 2016
  • Large frequency limiting distributions of two errors in realized covariance are investigated under noisy and non-synchronous high frequency sampling situations. The first distribution characterizes increased variance of the realized covariance due to noise for large frequency and the second distribution characterizes decreased variance of the realized covariance due to discretization for large frequency. The distribution of the combined error enables us to determine the sampling frequency which depends on a nuisance parameter. A consistent estimator of the nuisance parameter is proposed.

Uncertainty Analysis of Concrete Structures Using Modified Latin Hypercube Sampling Method

  • Yang, In-Hwan
    • International Journal of Concrete Structures and Materials
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    • v.18 no.2E
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    • pp.89-95
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    • 2006
  • This paper proposes a modified method of Latin Hypercube sampling to reduce the variance of statistical parameters in uncertainty analysis of concrete structures. The proposed method is a modification of Latin Hypercube sampling method. This analysis method uses specifically modified tables of random permutations of ranked numbers. In addition, the Spearman coefficient is used to make modified tables. Numerical analysis is carried out to predict the uncertainty of axial shortening in prestressed concrete bridge. Statistical parameters obtained from modified Latin Hypercube sampling method and conventional Latin Hypercube sampling method are compared and evaluated by a numeric analysis. The results show that the proposed method results in a decrease in the variance of statistical parameters. This indicates the method is efficient and effective in the uncertainty analysis of complex structural system such as prestressed concrete bridges.