• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.329-340
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• 1999

We consider a Bayesian forcasting method for the analysis of repeated surveys. It is assumed that the parameters of the superpopulation model at each time follow a stochastic model. We propose Bayesian prediction procedures for the finite population total under multiprocess dynamic generalized linear models. The multiprocess dynamic model offers a powerful framework for the modelling and analysis of time series which are subject to a abrupt changes in pattern. Some numerical studies are provided to illustrate the behavior of the proposed predictors.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.685-696
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• 2014

In this paper, we develop Bayesian inference of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation in the presence of auxiliary information under the balanced loss function. We compare the performance of the optimal Bayes estimator under the balanced loss function with ones of the classical ratio estimator and the usual Bayes estimator in terms of the posterior expected losses, risks and Bayes risks.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.255-269
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• 2017

This article explores the analysis of longitudinal surveys in which same units are investigated on several occasions. Multivariate exponential ratio type estimator has been proposed for the estimation of the finite population median at the current occasion in two occasion longitudinal surveys. Information on several additional auxiliary variables, which are stable over time and readily available on both the occasions, has been utilized. Properties of the proposed multivariate estimator, including the optimum replacement strategy, are presented. The proposed multivariate estimator is compared with the sample median estimator when there is no matching from a previous occasion and with the exponential ratio type estimator in successive sampling when information is available on only one additional auxiliary variable. The merits of the proposed estimator are justified by empirical interpretations and validated by a simulation study with the help of some natural populations.

• School Mathematics
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• v.16 no.3
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• pp.503-518
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• 2014

This study examines fourth graders engaged in three concrete activities involving sampling from finite populations. The first included a survey of popular foods for school meals. The second had them take samples from a box containing white and black marbles to predict how many white and black marbles were in the box. The final activity required them to predict how many times the Korean letter '가' would appear in a Korean story book. The results show that the participants can experience and notice different ideas related to samples and sampling in different activities. In the first activity, they acknowledged that samples are useful for obtaining the information about populations. A population survey is difficult and is not overly useful. In the second activity, they recognized that samples cannot be identical to their population but that the information from a group of samples is similar to the information of the population. In the last activity, they devised some ideas about random sampling even though the ideas were immature.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.391-411
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• 2010

For estimating the mean of a finite population, three classes of estimators using multi-auxiliary information with unknown means using two phase sampling in presence of non-response have been proposed with their properties. Asymptotically optimum estimator(AOE) in each class has been identified along with their mean squared error formulae. An empirical study is also given.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.537-548
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• 2011

In this paper following Singh et al. (2008), we propose a modified ratio-product type exponential estimator to estimate the finite population mean $\={Y}$ of the study variable y in presence of non-response in different situations viz. (i) population mean $\={X}$ is known, and (ii) population mean $\={X}$ is unknown. The expressions of biases and mean squared error of the proposed estimators have been obtained under large sample approximation using single as well as double sampling. Some realistic conditions have been obtained under which the proposed estimator is more efficient than usual unbiased estimators, ratio estimators, product estimators and exponential ratio and product estimators reported by Rao (1986) and Singh et al. (2010) are found to be more efficient in many situations.

• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.45-62
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• 2000

Rotation design is a sampling technique to reduce response burden and to estimate the population characteristics varying in time. Park and Kim(1999) discussed a generation of one-level rotation design which is called as {{{{r_1^m ~-r_2^m-1}}}} design has more applicable form than existing before. In the structure of {{{{r_1^m ~-r_2^m-1}}}} design, we derive the exact variances of generalized composite estimators for level, change and aggregate level characteristics of interest, and optimal coefficients minimizing their variances. Finally numerical examples are shown by the efficiency of alternative designs relative to widely used 4-8-4 rotation design. This is continuous work of Part Ⅰ studied by Park and Kim(1999).

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.481-487
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• 2015

In the product development stage, it is very important to ensure demanded quality level before moving to the full-scale manufacturing. For example, in developing guided weapons, live-fire tests are required to verify the final performance of the weapons which are very expensive. The quality evaluation of the guided weapons needs destructive testing, which makes it necessary to test as small number of samples as possible. This paper presents sampling inspection plans and calculating system for finite population guided weapons, which can meet the demanded quality level and confidence level with the minimum number of performance tests. The result of this paper can be useful for any kind of costly destructive testing.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.23-32
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• 2012

This paper deals with the bias and variance of regression coefficient estimators in a finite population. We derive approximate formulas for the bias, variance and mean square error of two estimators when we select a fixed-size inclusion probability proportional to the size sample and then estimate regression coefficients by the ordinary least square estimator as well as the weighted least square estimator based on the selected sample data. Necessary and sufficient conditions for the comparison of the two estimators in terms of variance and mean square error are suggested. In addition, a simple example is introduced to numerically compare the variance and mean square error of the two estimators.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.137-146
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• 2015

Singh (2013) considered the dual problem to the calibration of design weights to obtain a new generalized linear regression estimator (GREG) for the finite population total. In this work, we have made an attempt to suggest a way to use the dual calibration of the design weights in case of multi-auxiliary variables; in other words, we have made an attempt to give an answer to the concern in Remark 2 of Singh (2013) work. The same idea is also used to generalize the GREG estimator proposed by Deville and S$\ddot{a}$rndal (1992). It is not an easy task to find the optimum values of the parameters appear in our approach; therefore, few suggestions are mentioned to select values for such parameters based on a random sample. Based on real data set and under simple random sampling without replacement design, our approach is compared with other approaches mentioned in this paper and for different sample sizes. Simulation results show that all estimators have negligible relative bias, and the multivariate case of Singh (2013) estimator is more efficient than other estimators.