• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.671-683
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• 2021

Power allocation is a technique that evenly allocates samples for each stratum, although the overall efficiency of the allocation is less than that of optimal allocation, and it is often used as a square root proportional allocation in real survey. Also, considering the non-response that occurs in real survey, a larger sample size is used than that in the theoretical formula. In this study, in determining the sample size for each stratum, we study the new methods of allocating by adding information on the response rate per each stratum to power allocation method. The proposed allocation methods are compare with proportional, optimal, and square root proportional allocation in simulation. In addition, the comparison with the proportional and optimal allocation to which the response rate was added is examined through simulation. As a result, we examine the advantages and disadvantages of the allocation methods.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.535-543
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• 2003

A ranked set sample version of the sign test is proposed for testing hypotheses concerning the quantiles of a population characteristic by Kaur, et. al(2002). In this paper, we proposed the ranked set sample Wilcoxon signed rank test for quantiles under equal allocation. We obtain the asymptotic property and the asymptotic relative efficiencies of the proposed test statistic with respect to Wilcoxon signed rank test of simple random sample for quantiles under equal allocation. We calculate the ARE of test statistics, the proposed test statistic is more efficient than simple random sampling for all quantiles. The relative advantage of ranked set sampling is greatest at the median and tapers off in the tails.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.739-751
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• 2001

In this paper, a sample design is studied for 2000 forestry management survey of five types forestry , tree felling, gathering of pine mushroom, growing of nut trees, growing of wild flowers, and lumbering industry. We introduce population stratification and a modified stratified cut-off sampling which deal with determination of sample size, sample allocation, and estimation of total and variance of estimator. Substitution of sample units and imputation of nonresponse units are discussed for reducing the nonsampling errors.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.595-603
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• 2009

Typically the standard optimal allocation method distributes the sample for each stratum considering survey cost. In case of varying survey cost for each survey unit, we need to consider more practical allocation method. In other words, according to characteristics of an individual unit, we consider the optimal dynamic allocation method which first selects the survey unit having maximum value of benefit cost ratio. In terms of this, the proposed allocation method is different from standard optimal allocation method which allocate samples for each stratum and selects the random sample according to each size of sample. This paper is considered the dynamic optimal allocation method for the stratified randomized response technique which surveys for sensitive characteristic of survey units such as drug abuse, abortion, alcoholic. We prove the practical usefulness of proposed method using the numerical example.

• 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.

• Survey Research
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• v.6 no.1
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• pp.39-62
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• 2005

In this paper we investigate some concepts frequently called in sample surveys such as 'representative of sample' as well as 'consistency', 'unbiasedness', and 'efficiency' in estimation. The first is strongly related with sampling procedure including coverage rate of survey population, response rate in establishment survey, and recruit rate of final samples. The others, however, are concerned with both sampling design and corresponding estimators simultaneously. Whereas both consistency and unbiasedness are based on the representative sample, efficiency does not depend on the representative sample. The representative of sample can be increased by raising the rate of coverage, response and recruit as well. Consistency may be investigated according to variables of interest and auxiliary variables. The well-known raing-ratio weighting method is a method to increase consistency of auxiliary variables by means of matching population size in each cell. Efficiency is not directly related with the representative of sample, and allocation methods such as proportional and Neyman allocation in stratified sampling and post-stratification are all methods to increase the efficiency of estimation under the condition of satisfying the representative of sample.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.137-142
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• 2003

We discuss why sample design by mathematical programming can be beneficial to practical surveys. We illustrate some developments of software for sample design using mathematical programming in several statistical organizations. Also, we present certain restrictions on the use of mathematical programming.

• Journal of Families and Better Life
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• v.15 no.4
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• pp.237-252
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• 1997

The purpose of this study were to identify the income allocation structure of private education and child care and to analyze contributing factors income allocation structure of private education and child care for two children among Korean married couples. For these purposes total sample of 760 married couples those having two children of both pre-school aged and elementary school aged wee selected and total sample was divided into two groups by first child's school aged; those were pre-school aged(375) and elementary school aged (385) Statistics were frequencies means percentile and Tobit and OLS analysis. The results were as follows. First 268 households among the households those having the first child of pre-school aged spent 11% of income for private education while 29 households spent 7% of income for child care. 348 households among the households having the first child of elementary school aged spent 11% of income for private education. Second pre=-school aged' contri uting factors to income allocation struture of private education and child care were household family type family size home ownership and financial asset amount and elementary school-aged factors were satisfaction of family relationship and financial asset.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1503-1520
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• 2013

For multi-line insurance companies, allocating the risk capital to each line is a widely-accepted risk management exercise. In this article we consider several applications of the Euler capital allocation. First, we propose visual tools to present the diversification and the line-wise performance for a given loss portfolio so that the risk managers can understand the interactions among the lines. Secondly, on theoretical side, we prove that the Euler allocation is the directional derivative of the marginal or incremental allocation method, an alternative capital allocation rule in the literature. Lastly, we establish the equivalence between the mean-shortfall optimization and the RORAC optimization when the risk adjusted capital is the expected shortfall, and show how to construct the optimal insurance business mix that maximizes the portfolio RORAC. An actual loss sample of an insurance portfolio is used for numerical illustrations.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.13-24
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• 2001

In this paper, we investigated mean estimation methods in two-phase sampling. Under the fixed expected cost we reviewed the optimal sample sizes, minimum variances and approximate unbiased variance estimators for usual ratio estimator, stratified sample mean with proportional allocation and Rao's allocation of the second phase sample. Also we proposed combined ratio estimator, which uses both ratio estimation and stratification and derived optimal sample size, minimum variance and unbiased variance estimator. Through a limited simulation study, we compared estimators by design effects and came to know that ratio estimator is more efficient than stratified sample mean in some cases and inefficient in the other cases, but combined ratio estimator is more efficient than others in most cases.