• Journal of the Korean Statistical Society
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• v.35 no.4
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• pp.377-395
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• 2006

In this paper we have suggested a family of chain estimators of the population mean $\bar{Y}$ of a study variate y using two auxiliary variates in two phase (double) sampling assuming that the coefficient of variation of the second auxiliary variable is known. It is well known that chain estimators are traditionally formulated when the population mean $\bar{X}_1$ of one of the two auxiliary variables, say $x_1$, is not known but the population mean $\bar{X}_2$ of the other auxiliary variate $x_2$ is available and $x_1$ has higher degree of positive correlation with the study variate y than $x_2$ has with y, $x_2$ being closely related to $x_1$. Here the classes are constructed when the population mean $\bar{X}_1\;of\;X_1$ is not known and the coefficient of variation $C_{x2}\;of\;X_2$ is known instead of population mean $\bar{X}_2$. Asymptotic expressions for the bias and mean square error (MSE) of the suggested family have been obtained. An asymptotic optimum estimator (AOE) is also identified with its MSE formula. The optimum sample sizes of the preliminary and final samples have been derived under a linear cost function. An empirical study has been carried out to show the superiority of the constructed estimator over others.

• Journal of the Korea Safety Management & Science
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• v.13 no.2
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• pp.195-202
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• 2011

The study focuses on the Repeated Measurements Design (RMD) which observations are periodically made for identical subjects within definite time periods. One of the purposes of this design is to monitor and keep track of replicated records within regular period over years. This paper also presents the classification models of RMD that is developed according to the number of factors in Between-Subject (BS) variates and Within-Subject (WS) variates. The types of models belong to each number of factors: One factor is 0BS 1WS. Two factors are 1BS 1WS and 0BS 2WS. Three factors are 1BS 2WS and 2BS 1WS. Lastly, the four factors include model of 2BS 2WS In addition, the study explains the generation mechanism of models for RMD using Generalizability Design (GD). GD is a useful method for practitioners to identify linear model of experimental design, since it generates a Venn diagram. Lastly, the research develops three types of 1BS 2WS RMDs with crossed factors and nested factors. Those are random models, mixed models and fixed models and they are presented by using Generalizability Design, $(S:A{\times}B){\times}C$. Moreover, the example of applications and its implementation steps of models developed in the study are presented for better comprehension.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.471-478
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• 2012

In this paper, we present a permutation test to select the number of pairs of canonical variates in canonical correlation analysis. The existing chi-squared test is known to be limited to normality in use. We compare the existing test with the proposed permutation test and study their asymptotic behaviors through numerical studies. In addition, we connect canonical correlation analysis to regression and we we show that certain inferences in regression can be done through canonical correlation analysis. A regression analysis of real data through canonical correlation analysis is illustrated.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.389-396
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• 2009

Approximations for the null distribution of a test statistic arising in multivariate analysis to test homogeneity of variances and a mixture of two beta distributions by making use of a product of beta baseline density function and a polynomial adjustment, so called beta-polynomial density approximant, are discussed. Explicit representations of density and distribution approximants of interest in each case can easily be obtained. Beta-polynomial density approximants produce good approximation over the entire range of the test statistic and also accommodate even the bimodal distribution using an artificial example of a mixture of two beta distributions.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.155-164
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• 2011

This paper suggests two ratio-cum-product estimators of finite population mean using known coefficient of variation and co-efficient of kurtosis of auxiliary characters. The bias and mean squared error of the proposed estimators with large sample approximation are derived. It has been shown that the estimators suggested by Upadhyaya and Singh (1999) are particular case of the suggested estimators. Almost ratio-cum product estimators of suggested estimators have also been obtained using Jackknife technique given by Quenouille (1956). An empirical study is also carried out to demonstrate the performance of the suggested estimators.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.265-275
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• 1998

In testing the partial association between two variables after controlling for the S levels of a third factor, the Mantel and Haenszel (1959) statistic is often used. Since the statistic is based on the asymptotic distribution of the sum X of S hypergeometric variates, a guideline for the minimum requirements for the application of the statistic is useful. Mantel and Fleiss (1980) developed a criterion based on the guideline for the Pearson's $X^2$ statistic. The criterion requires the distance from the expected value to the closer bound of X to be at least five. The Mantel-Fleiss (MF) criterion was studied through a simulation using the hypergeometric sampling scheme. The criterion is not satisfactory. The size of statistic exceeded nominal 0.05 level nearly 1/5 of the cases even when the criteion is met. However, the results show that the statistic is much more unstable and conservative when the criterion is not met.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.953-961
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• 2006

Let's say that we are given a k number of random variables following Poisson distribution that are individually dependent and which forms multivariate Poisson distribution. We particularly dealt with a method of creating random numbers that satisfies the covariance matrix, where the elements of covariance matrix are parameters forming a multivariate Poisson distribution. To create such random numbers, we propose a new algorithm based on the method reducing the number of parameter set and deal with its relationship to the Park et al.(1996) algorithm used in creating multivariate Bernoulli random numbers.

• Proceedings of the Korea Society for Simulation Conference
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• 1992.10a
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• pp.8-8
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• 1992

Through a simulation experiment, often an experimenter is concerned with estimating the system parameters of the linear model consisting of m design points from the outputs oft the simulation model. To improve the estimation of the system parameters and reliability of these estimators, appropriate simulation techniques have been developed. For the first order linear model, Schruben and Margolin (1978) exploited the random number assignment rules which uses a combination of common random numbers and antithetic streams in a simulation experiment designed to estimate the system parameters when the design matrix of simulation model admits orthogonal blocking into two blocks. Nozari, Arnold and Pegden (1984) developed a method for appliying the method of control variates to the situation of the linear model having multiple design points. This talk deals with a different way of utilizing controls under the correlation induction strategy of Schruben and Margolin's to improve the simulation efficiency, and presents a procedure for obtaining the estimators of the system parameters analytically. Simulation results on a selected simulation model indicate a promising evidence that a proposed method may yield better results than Schruben and Margolin's method.

• Journal of Korea Water Resources Association
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• v.44 no.2
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• pp.85-96
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• 2011

Probability plotting position is generally used for the graphical analysis of the annual maximum quantile and the estimation of exceedance probability to display the fitness between sample and an appropriate probability distribution. In addition, it is used to apply a specific goodness of fit test. Plotting position formula to define the probability plotting position has been studied in many researches. Especially, the GEV distribution which is an important probability distribution to analyze the frequency of hydrologic data was popular. In this study, the theoretical reduced variates are derived using the mean value of order statistics to derived an appropriate plotting position formula for the GEV distribution. In addition, various forms of plotting position formula considering various sample sizes and coefficients of skewness related with shape parameters are applied. The parameters of plotting position formulas are estimated using the genetic algorithm. The accuracy of derived plotting position formula is estimated by the errors between the theoretical reduced variates and those by various plotting position formulas including the derived ones in this study. As a result, the errors by derived plotting position formula is the smallest at the range of shape parameter with -0.25~0.10.

• Korean Journal of Adult Nursing
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• v.13 no.3
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• pp.463-475
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• 2001

The purpose of the study was to examine if individual characteristics and experiences related to smoking behavior identified from the literature were significantly associated with behavior-specific cognitions and affect in the same way as presented in Pender's Revised Health Promotion Model(Pender, 1996). The subjects selected for this study were 400 college students enrolled in more than 10 colleges located in Seoul and Kyunggi-Inchon province. According to the study results, personal factors (i.e., perceived health status, the past history of disease, and symptoms related to smoking) and related behavior (i.e., the degree of alcohol consumption, and exercise) are significantly associated with behavior-specific cognitions and affect (i.e., perceived barriers to smoking cessation, perceived self-efficacy, and perceived benefits of smoking cessation). The canonical correlation between two groups of variables was .59, and it turned out to be statistically significant. Thirty-four percent of variance of the relationship between two group of variables was explained by two canonical variates which turned out to be significant in the study results. The result could be interpreted from the view of psycho-social area as follows: overall, this study includes important variables which explain the association between two groups of variables.