• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.609-620
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• 2005

In the secondary data analysis for categorical data, situations often arise in which the estimated cell variances are available, but not the full matrix of variances. In this case researchers are often inclined to use Pearson-type test statistics for homogeneity. However, for a complex sample observed cell proportions are not distributed as multinomial and Pearson-type test statistic generally is not distributed asymptotically as chi-square distribution. This paper evaluates powers for Wald test and Pearson-type test and the first order corrected test of Pearson-type test for homogeneity. The resulting power curves indicate that as the misspecification effect increases, the amount of inflation of significance level and the loss of power Pearson-type test are getting more severe.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.33-38
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• 2000

Krishnamoorphy and Raghavarao(1993) invented exact binomial and asymptotically normal test procedures for truthful answering in the repeated randomized response models under the assumption that two repeated response measures are independent. Under the same assumption, Lakshmi and Raghavarao(1992) suggested asymptotic chi-square test for respondents' truthful answering in the same models. In this article we detect the factors and the conditions with which two response variables might be independent, and find the condition for independence in the repeated randomized response models with considering untruthful answer. But, the condition of independence make the randomized model no meaning. Under the assumption of conditional independence between two response variables, we can apply the same logical statements on deriving the tests for truthful answering in the repeated randomized response models as in Krishnamoorphy and Raghavarao(1993).

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.193-209
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• 2017

The power Lindley distribution with some of its properties is considered in this article. Maximum likelihood, least squares, maximum product spacings, and Bayes estimators are proposed to estimate all the unknown parameters of the power Lindley distribution. Lindley's approximation and Markov chain Monte Carlo techniques are utilized for Bayesian calculations since posterior distribution cannot be reduced to standard distribution. The performances of the proposed estimators are compared based on simulated samples. The waiting times of research articles to be accepted in statistical journals are fitted to the power Lindley distribution with other competing distributions. Chi-square statistic, Kolmogorov-Smirnov statistic, Akaike information criterion and Bayesian information criterion are used to access goodness-of-fit. It was found that the power Lindley distribution gives a better fit for the data than other distributions.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.8
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• pp.1254-1265
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• 2000

The purpose of this study was to examine the differences in clothing purchase behaviors among groups determined by subjective and chronological ages. The subjects of this study were teens and adults of up to 59 years of age residing in Seoul and the Metropolitan areas. Out of 700, 578 were used for final data analysis, Chi-square analysis, ANOVA, Duncans multiple range test and descriptive statistics were used for statistics analysis. The following results were found: the subjects were divided into 3 groups based on the subjective age. All age groups tended to perceive subjective age similar to chronological one. Regarding evaluative criteria for selecting clothes and stores, significant differences were found among all chronological and subjective age groups. By chronological age levels, significant differences were found among subjective age groups except the teens in the evaluative criteria used for purchasing clothing. Significant differences were found among subjective age group in all chronological age brackets in store selection criteria.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.12 no.2
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• pp.81-95
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• 2008

In this paper, the negative Polya-Eggenberger distribution has been introduced by compounding negative binomial distribution with beta distribution of I-kind which generates a number of univariate contagious or compound (or mixture of) distributions as its particular cases. The distribution is unimode, over dispersed and all of its positive and negative integer moments exist. The difference equation of the proposed model shows that it is a member of the Ord's family of distribution. Some of its interesting properties have been explored besides different methods of estimation been discussed. Finally, the parameters of the proposed model have been estimated by using a computer programme in R-software. Application of the proposed model to some data, available in the literature, has been given and its goodness of fit demonstrated.

• Journal of the Korean Society of Costume
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• v.25
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• pp.201-213
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• 1995

The purposes of the study were (1) to seg-ment the female college apparel market based on fashion life style and (2) to develop a pro-file of each segment regard to self-concept, shopping orientation and patronage behavior. The data were collected through questionnaire by random sample of 526 female college students. By cluster analysis of lifestyle factors, three groups were identified, (fashion leaders, fashion followers and fashion aversion) Three groups were then compared through multivariate analysis of variance and chi-square statistics on 10 self-concept variables, 6 shopping statistics on 10 self-concept variables, 6 shopping orientation factors and 1 patronage behavior variable. Significant difference were found among the three groups on all these variables which indi-cate that fashion lifestyle can be a useful base for segmenting female apparel market and these groups are unique in terms of self-con-cept, shopping orientation and patronage behavior.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.285-299
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• 2020

In many cases, we are interested in identifying independence between variables. For continuous random variables, correlation coefficients are often used to describe the relationship between variables; however, correlation does not imply independence. For finite discrete random variables, we can use the Pearson chi-square test to find independency. For the mixed type of continuous and discrete random variables, we do not have a general type of independent test. In this study, we develop a independence test of a continuous random variable and a discrete random variable without assuming a specific distribution using kernel density estimation. We provide some statistical criteria to test independence under some special settings and apply the proposed independence test to Pima Indian diabetes data. Through simulations, we calculate false positive rates and true positive rates to compare the proposed test and Kolmogorov-Smirnov test.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.51-73
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• 2002

Empirical likelihood ratio method is a new technique in nonparametric inference developed by A. Owen (1988, 2001). Sometimes empirical likelihood has difficulties to define itself. As such a case in point, we discuss the way to define a modified empirical likelihood for the location of symmetry using well-known points of symmetry as a side conditions. The side condition of symmetry is defined through a finite subset of the infinite set of constraints. The modified empirical likelihood under symmetry studied in this paper is to construct a constrained parameter space $\theta+$ of distributions imposing known symmetry as side information. We show that the usual asymptotic theory (Wilks theorem) still hold for the empirical likelihood ratio on the constrained parameter space and the asymptotic distribution of the empirical NPMLE of difference of two symmetric points is obtained.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.211-224
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• 2003

Good's lambda test, a permutation test used to detect the changes of microorganism composition under two pathological conditions, has been quite popular for studying the micro-flora responsible for periodontal disease. A vast number of different micro-flora in the mouth renders the traditional chi-square test inapplicable. The main purpose of this paper is to evaluate the power of this test so that the sample size can be determined at the design stage. The robustness of this test and its comparison to two other intuitive tests are also presented. It is found that a permutation test based on likelihood ratio is more powerful than the lambda test in our simulated cases.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.641-649
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• 2017

Multivariate confidence regions were defined using a chi-square distribution function under a normal assumption and were represented with ellipse and ellipsoid types of bivariate and trivariate normal distribution functions. In this work, an alternative confidence region using the multivariate quantile vectors is proposed to define the normal distribution as well as any other distributions. These lower and upper bounds could be obtained using quantile vectors, and then the appropriate region between two bounds is referred to as the quantile confidence region. It notes that the upper and lower bounds of the bivariate and trivariate quantile confidence regions are represented as a curve and surface shapes, respectively. The quantile confidence region is obtained for various types of distribution functions that are both symmetric and asymmetric distribution functions. Then, its coverage rate is also calculated and compared. Therefore, we conclude that the quantile confidence region will be useful for the analysis of multivariate data, since it is found to have better coverage rates, even for asymmetric distributions.