• 산업경영시스템학회지
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• 제46권3호
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• pp.251-258
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• 2023

This study (1) explored spatio-temporal population distribution patterns in Jeonju by using emerging hot spot analysis and (2) identified the influential factors to determine the spatio-temporal patterns by using multinomial logit model. The major findings are as follows. First, the results of emerging hot spot analysis indicated that the 100*100m grid in the urban area of Jeonju was found to have a category of hot spots, whereas most of the cold spot series was concentrated in the outskirts of the city. Also, new towns such as Jeonju Eco City, Jeonbuk Innovation City, and Hyocheon District were persistent or intensifying hot spots, Third, the results of multinomial logit model revealed that the factors influencing deterrmining the spatio-temporal patterns were accessibility to schools, hospitals, parks, and walfare services. This study offered a deeper understanding of urbanization and regional changes in Jeonju, and important information for urban planning.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.81-92
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• 2007

This paper consider trinomial group testing concerned with classification of N given units into one of k disjoint categories. In this paper, we propose Bayesian inference for estimating individual category proportions using the trinomial group testing model proposed by Bar-Lev et al. (2005). We compared a relative efficience (RE) based on the mean squared error (MSE) of MLE and Bayes estimators with various prior information. The impact of different prior specifications on the estimates is also investigated using selected prior distribution. The impact of different priors on the Bayes estimates is modest when the sample size and group size we large.

• 품질경영학회지
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• 제26권1호
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• 한국유통학회지:유통연구
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• 제14권1호
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• pp.45-65
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• 2009

정보통신기기의 발달과 생활환경의 변화는 소비자의 구매유형을 다양화 시키는 역할을 하였다. 소비자의 구매유형에 따른 시장점유율의 변화는 관련 기업 뿐 아니라 정책 관계자들에 있어서도 매우 주요한 이슈로 떠오르고 있다. 본 연구는 2007년 설문조사 결과를 이용하여, 이중생잔모형을 고려한 다중로짓모형 분석과 구매형태별 시장점유율을 예측하였다. 시장규모 및 점유율에 대한 예측이 다양한 관련 주체의 경제적 효율성 및 형평성의 실현에 있어 중요한 사안임을 감안한다면, 본 연구의 결과 및 연구의의는 다음의 세 가지로 요약될 수 있다. 첫째, 소비자의 구매유형의 선택 형태는 생잔효과(Cohort Effect)를 고려하여야 한다. 연령대별 선호 구매형태 및 충성도가 다르며 또한 생잔효과를 감안한 시장점유율은 매우 유동적일 것으로 판단된다. 둘째, 기존의 온라인의 구매형태의 감소는 빠른 속도로 온라인 구매형태로 이전할 것이며, 동일 온라인 구매형태에 있어서도 온라인, 인터넷, TV 홈쇼핑 및 기타 간의 시장 분할도 2013년 경 안정된 비율을 유지할 것이다. 셋째, 시간의 경과에 따른 연령별 생잔효과의 분석에서 현재의 연령대가 차후 연령으로 진행하더라도 구매방법을 획기적으로 바꾸기보다는 현재의 소비행태를 비슷하게 유지하는 효과가 있는 것으로 판단되며, 이는 나이를 먹는 것(Aging)에도 일종의 사슬효과(Chain Effect)가 있는 것으로 해석할 수 있다. 본 연구는 구매형태를 고려한 시장점유율을 분석할 수 있는 방법론을 적용하였다는 측면과 생잔효과를 고려한 다양한 관련 주체들의 활동에 시사점을 줄 수 있다는 의의를 가진다.

• 대한전기학회논문지:전력기술부문A
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• 제52권7호
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• pp.401-406
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• 2003

Electric power industries in several countries are currently undergoing major changes, mainly represented by the privatizations of the power plants and distribution systems. Reliable operations of the power plants directly contribute to the revenue increases of the generation companies in such competitive environments. Strategic optimizations should be performed between the levels of the reliabilities to be maintained and the various preventive maintenance costs, which require the accurate estimations of the power plant reliabilities. However, accurate estimations of the power plant reliabilities are often limited by the lack of accurate power plant failure data. A power plant is not supposed to be failed that often. And if it fails, its impact upon the power system stability is quite substantial in most cases, setting aside the significant revenue losses and lowered company images. Reliability assessment is also important for Independent System Operators(ISO) or Market Operators to properly assess the level of needed compensations for the installed capacity based on the availability of the generation plants. In this paper, we present a power plant reliability estimation technique that can be applied when the failure data is insufficient. Median rank and Weibull distribution are used to accommodate such insufficiency. The Median rank is utilized to derive the cumulative failure probability for each ordered failure. The Weibull distribution is used because of its flexibility of accommodating several different distribution types based on the shape parameter values. The proposed method is applied to small size failure data and its application potential is demonstrated.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.523-532
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• 1999

Generalized linear models have various applications for data arising from many kinds of statistical studies. Although the response variable is generally assumed to be generated from a wide class of probability distributions we focus on count data that are most often analyzed using binomial models for proportions or poisson models for rates. The methods and results presented here also apply to many other categorical data models in general due to the relationship between multinomial and poisson sampling. The novelty of the approach suggested here is that all conditional distribution s can be specified directly so that staraightforward Gibbs sampling is possible. The prior distribution consists of two stages. We rely on a normal nonconjugate prior at the first stage and a vague prior for hyperparameters at the second stage. The methods are demonstrated with an illustrative example using data collected by Rosenkranz and raftery(1994) concerning the number of hospital admissions due to back pain in Washington state.

• International Journal of Contents
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• 제2권2호
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• pp.17-24
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• 2006

The work presented in this paper is divided into two parts. The first part presents finite urn problems which generate truncated negative binomial random variables. Some combinatorial identities that arose from the negative binomial sampling and truncated negative binomial sampling are established. These identities are constructed and serve important roles when we deal with these distributions and their characteristics. Other important results including cumulants and moments of the distributions are given in somewhat simple forms. Second, the distributions of the maximum of two chi-square variables and the distributions of the maximum correlated F-variables are then derived within the negative binomial sampling scheme. Although multinomial theory applied to order statistics and standard transformation techniques can be used to derive these distributions, the negative binomial sampling approach provides more information and deeper insight regarding the nature of the relationship between the sampling vehicle and the probability distributions of these functions of chi-square variables. We also provide an algorithm to compute the percentage points of these distributions. We supplement our findings with exact simple computational methods where no interpolations are involved.

• International Journal of Contents
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• 제3권1호
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• pp.1-8
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• 2007

The distributions of the minimum correlated F-variable arises in many applied statistical problems including simultaneous analysis of variance (SANOVA), equality of variance, selection and ranking populations, and reliability analysis. In this paper, negative binomial sampling technique is employed to derive the distributions of the minimum of chi-square variables and hence the distributions of the minimum correlated F-variables. The work presented in this paper is divided in two parts. The first part is devoted to develop some combinatorial identities arised from the negative binomial sampling. These identities are constructed and justified to serve important purpose, when we deal with these distributions or their characteristics. Other important results including cumulants and moments of these distributions are also given in somewhat simple forms. Second, the distributions of minimum, chisquare variable and hence the distribution of the minimum correlated F-variables are then derived within the negative binomial sampling framework. Although, multinomial theory applied to order statistics and standard transformation techniques can be used to derive these distributions, the negative binomial sampling approach provides more information regarding the nature of the relationship between the sampling vehicle and the probability distributions of these functions of chi-square variables. We also provide an algorithm to compute the percentage points of the distributions. The computation methods we adopted are exact and no interpolations are involved.

• 한국수산과학회지
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• 제54권1호
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• pp.80-89
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• 2021

In Korea, marine fish landed at ports are randomly sampled on a periodic basis (e.g., daily or weekly), and body sizes (e.g., lengths and weights) of those sampled fish are measured. The motivation for our study is whether or not such measurements reflect the size distribution, especially the length distribution of fish landed (= a population), because such length measurements are key data for a length-based assessment model. The current sampling method is to sample fish landed at ports by body size group (e.g., very small, small, medium, large, very large), using the sampling weights as the number of boxes by body size group. In this study, we showed that length composition data about fish sampled by the current method did not represent the length frequency distribution of the fish landed, and suggested that an alternative sampling method should be applied of using the sampling weights as the number of fish landed by body size group. We also introduced a method for determining an appropriate sample size.

• 유통과학연구
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• 제22권8호
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• pp.125-135
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• 2024

Purpose: This paper aims to reveal similarities and differences in behaviors in negative consumer-brand relationships. Thus, we focused on consumer motivation which includes intensity and direction of behaviors. The motivation for negative customer behavior has been discussed in the context of brand hate, but there is only limited research that has tried to measure it using quantitative methods. We are trying to measure customers' motivation in negative consumer-brand relationships and reveal the relationship between in-field customers' negative behaviors. Research design, data, and methodology: We adopt Reactive-Proactive aggression to measure the motivation of customers' behaviors in a negative consumer-brand relationship. Also, to reveal the relationship between in-field behavior and customer aggression, we survey Korean game communities to reactive-proactive aggression and behaviors, whether they participate, in each observed behavior during the serial negative consumer movements that occurred in the Korean game industry. As a methodology, we run multinomial logistic regression. Results: We observed 9 behaviors in this case, and we found that reactive-proactive aggression is related to participation and motivation of these behaviors. Conclusions: We suggest the potential of reactive-proactive aggression as motivation for customers' complex negative behaviors. Based on this potential, we hope reactive-proactive aggression could be used to reveal similarities and differences in behaviors in negative consumer-brand relationships.