• 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.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.377-385
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• 2015

After performing callback for nonresponses in sample survey, we present an estimator of regression form using an auxiliary variable and a variance estimator using replicate method. Parametric inference method of the response probability is also presented. We research an unbiased estimator of high efficiency for the population mean and a variance estimator with consistency under callback. We also prove the validity of the theory through the simulation.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.553-566
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper considers design-based estimators of the parameters of these variance functions based on the within-unit sample variances. This paper devotes to: (1) define an error scale factor $\delta$; (2) develop estimators of the parameters of the linear measurement error variance function of the true values under large-sample and small-error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U.S. Third National Health and Nutrition Examination Survey (NHANES III).

• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.34-41
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper develops estimators of the parameters of a linear measurement error variance function based on wi thin-unit sample variaoces. This paper devotes to: (1) define measurement error scale factor $\delta$: (2) develop estimators of the parameters of the 1inear measurement error variance function under stratified multistage sampling design and small error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U S Third National Health and Nutrition Examination Survey(NHANES III)

• Korean Journal of Fisheries and Aquatic Sciences
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• v.24 no.4
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• pp.248-254
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• 1991

The demersal fishes of Asan Bay were collected by an otter trawl in August 1990 to determine optimum sample size for the analysis of community structure. A total of 17 species comprising 957 individuals and 21,840 grams in biomass was collected. Predominant species were Cynoglossus joyneri, Thrissa koreana, Hexagrammos otakii and Johnius belengerii. Coefficients of variation for fish numbers in ten replicate tows ranged from 2.2 to $385.1\%$ for four abundant species and from 52.2 to $162.0\%$ when all species were considered. The cumulative number of species increased rapidly until 4 hauls, and less than 1 species per haul in average was added thereafter. The cumulative diversity index reached nearly an asymptote value when three of samples were combined. Variance in the number of individuals diminished as the sample size increased. The ratio of variance to mean numbers (dispersion index) was not significantly different from the unity when first 4-haul samples were combined. Four of 20-minute trawl haul are proposed to be a proper sampling size for the unbiased estimation of abundance in the study area.