• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.277-289
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• 2003

We propose modified exact inferential methods in logistic regression model. Exact conditional distribution in logistic regression model is often highly discrete, and ordinary exact inference in logistic regression is conservative, because of the discreteness of the distribution. For the exact inference in logistic regression model we utilize the modified P-value. The modified P-value can not exceed the ordinary P-value, so the test of size $\alpha$ based on the modified P-value is less conservative. The modified exact confidence interval maintains at least a fixed confidence level but tends to be much narrower. The approach inverts results of a test with a modified P-value utilizing the test statistic and table probabilities in logistic regression model.

• Journal of Distribution Science
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• v.14 no.5
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• pp.27-37
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• 2016

Purpose - Prior theories expect a lower marginal value of cash for weak governance firms. To test this hypothesis, we examine the relationship between corporate governance structures and marginal cash values in Korean retail firms. Research design, data, and methodology - We estimate marginal cash values based on the model of Faulkender & Wang (2006). The retail firms listed in Korean Stock Exchange from 2005 to 2013 are analyzed. Corporate governance scores are provided by Korean Corporate Governance Services. Results - We show a higher marginal value of cash for the weak governance retail firms in terms of total governance score. Our analysis on a detailed set of governance scores generally confirms this tendency. Yet, a higher marginal cash value is obtained for the firms with better board structures and dividend policies. Conclusions - Our findings argue against the agency view of cash policy predicting a negative relationship between corporate governance scores and marginal cash values. A low marginal value of cash, widely observed in the sample firms, also supports severe resource diversion problem in Korean corporations.

• Journal of Distribution Science
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• v.6 no.2
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• pp.61-79
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• 2008

This paper studies on the influence of customers' perceived store crowding, shopping emotions, shopping value, and repatronage intention. The result is as follows. First, human crowding and spatial crowding influence two dimensions of shopping emotions: positive shopping emotions and negative shopping emotions. Especially human crowding influences positively on positive shopping emotions while spatial crowding influence positively on negative shopping emotions, which means that shoppers visiting stores feel negative shopping emotions toward spatial crowding, but that shoppers' crowding in stores can arose positive shopping emotions which intrigue interest and liveliness. Second, positive shopping emotions and negative shopping emotions influence two dimensions of shopping values: hedonic shopping value and utilitarian shopping value. Especially positive shopping emotion influences positively on utilitarian shopping value and hedonic shopping value while negative shopping emotion influence negatively on utilitarian shopping value and hedonic shopping value. Third, the result shows that shoppers' shopping values influences positively on repatronage intention.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.153-186
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• 2007

It is well known that the distributional properties of financial asset returns exhibit fatter-tails and skewer-mean than the assumption of normal distribution. The correct assumption of return distribution might improve the estimated performance of the Value-at-Risk(VaR) models in financial markets. In this paper, we estimate and compare the VaR performance using the RiskMetrics, GARCH and FIGARCH models based on the normal and skewed-Student-t distributions in two daily returns of the Korean Composite Stock Index(KOSPI) and Korean Won-US Dollar(KRW-USD) exchange rate. We also perform the expected shortfall to assess the size of expected loss in terms of the estimation of the empirical failure rate. From the results of empirical VaR analysis, it is found that the presence of long memory in the volatility of sample returns is not an important in estimating an accurate VaR performance. However, it is more important to consider a model with skewed-Student-t distribution innovation in determining better VaR. In short, the appropriate assumption of return distribution provides more accurate VaR models for the portfolio managers and investors.

• Journal of Distribution Science
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• v.20 no.2
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• pp.1-9
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• 2022

Purpose: The online distribution channel has attracted the attention of retailers by potential impact on consumers' purchase intention. The objectives of this study are to provide an insight into how to encourage consumers' impulse buying behavior on commercial website as well as attempts to reveal factors that influence consumers' impulsive buying behavior in the online shopping environment. Research design, data and methodology: The research framework is based on the stimulus-organism-response (S-O-R) framework. The Meta-analysis method carried out the research, gathering data from 37 published studies. Results: The research findings suggest that intrinsic motivations such as perceived ease of use, perceived enjoyment, and online flow experience play a significant role in boosting consumers' hedonic value when buying and online. In addition, these findings help online retailers use appropriate marketing stimuli such as offering pricing incentives, promotion tactics, and improved communication effectiveness. Also, obtaining a better grasp of how to build a website to improve the consumer experience generally helps consumers feel the urge to buy impulsively and act without hesitation. Conclusions: This research confirms a direct positive relationship between marketing stimuli and hedonic shopping value, which may support an applied theoretical framework for future research and provide managerial implications for retailers in online distribution channels.

• Wind and Structures
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• v.33 no.6
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

• Journal of Distribution Science
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• v.20 no.6
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• pp.1-10
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• 2022

Purpose: This research proposes a new modified Recency-Frequency-Monetary (RFM) model by extending the model with spatial analysis for supporting decision-makers in discovering the promotional target market. Research design, data and methodology: This quantitative research utilizes data-mining techniques and the RFM model to cluster a university's provider schools. The RFM model was modified by adapting its variables to the university's marketing context and adding a district's potential (D) variable based on heatmap analysis using Geographic Information System (GIS) and K-means clustering. The K-prototype algorithm and the Elbow method were applied to find provider school clusters using the proposed RFM-D model. After profiling the clusters, the target segment was assigned. The model was validated using empirical data from an Indonesian university, and its performance was compared to the Customer Lifetime Value (CLV)-based RFM utilizing accuracy, precision, recall, and F1-score metrics. Results: This research identified five clusters. The target segment was chosen from the highest-value and high-value clusters that comprised 17.80% of provider schools but can contribute 75.77% of students. Conclusions: The proposed model recommended more targeted schools in higher-potential districts and predicted the target segment with 0.99 accuracies, outperforming the CLV-based model. The empirical findings help university management determine the promotion location and allocate resources for promotional information distribution and student recruitment.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1263-1274
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• 2013

VaR (value at risk), which represents the expectation of the worst loss that may occur over a period of time within a given level of confidence, is currently used by various financial institutions for the purpose of risk management. In the majority of previous studies, the probability of return has been modeled with normal distribution. Recently Chen et al. (2010) measured VaR with asymmetric Laplacian distribution. However, it is difficult to estimate the mode, the skewness, and the degree of variance that determine the shape of an asymmetric Laplacian distribution with limited data in the real-world market. In this paper, we show that the VaR estimated with (symmetric) Laplacian distribution model provides more accuracy than those with normal distribution model or asymmetric Laplacian distribution model with real world stock market data and with various statistical measures.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.2
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• pp.223-230
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• 2012

The strength of brittle material has commonly been characterized by a normal distribution or Weibull distribution, but it may fit the gamma distribution for some material. The use of an extreme value distribution is proper when the largest values of a set of stresses dominate the failure of the material. This paper presents a formula for reliability estimation based on stress-strength interference theory that is applicable when the strength of material is distributed like a gamma distribution and the stress is distributed like an extreme value distribution. We verified the validity of the equation for the reliability estimation by examining the relationships among the factor of safety, the coefficient of variation, and the reliability. The required minimum factor of safety and the highest allowable coefficient of variation of stress can be estimated by choosing an objective reliability and estimating the reliabilities obtained for various factors of safety and coefficients of variation.

• Journal of Distribution Science
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• v.22 no.9
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• pp.129-139
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• 2024

Purpose: This research aims to examine the impact of social media marketing on buy decisions in Indonesia's B2B market, considering the mediating roles of brand image, perceived quality, and perceived value in relation to interactive flat panel display technology. To better understand technology adoption and distribution, we utilize the innovation diffusion theory. Research Design, Data and Methodology: The Decision-Making Unit of each organization that buy interactive flat panel display technology conducted an empirical survey of 82 participants. The quantitative research design analyzed the data utilizing the PLS-SEM model. outcome: This research reveals that social media marketing significantly impacted perceived quality, brand image, perceived value, and buy decisions. The research also found that perceived quality does not significantly impact buy decisions, but perceived value and brand image significantly impacted buy decisions. This research contributes to understanding the key factorsinfluencing buy decisionsin Indonesia's B2B market. Conclusion: Thisresearch concludesthat B2B consumers in Indonesia are less concerned about product quality but prioritize the value they receive when purchasing interactive display technology. Social media marketing could impacted the distribution of interactive display technology in Indonesia's B2B market by affecting the DMU's purchasing decisions. Brandsshould leverage social media marketing to positively impact theirsuccess.