• Journal of the Korean Data and Information Science Society
• /
• v.28 no.2
• /
• pp.327-337
• /
• 2017

In this study we compared three models for small area estimation, Fay-Herriot, Hierarchical Bayses model and spatio-temporal model about charter, monthly rent price index. Charter, monthly rent price of Korea are important issue in these days. Because housing type rapidly changes from self to charter and monthly rent. The accuracy of the estimation was checked on four scales, that is ARB, ASRB, AAB, ASD. In this result, the spatio-temporal model among applied models has most optimal scales about small area estimation of charter and monthly rent index.

• KDI Journal of Economic Policy
• /
• v.36 no.3
• /
• pp.61-93
• /
• 2014

This paper analyzes Onbid car auction data by employing various methods, including structural estimation, to identify main factors which decides auction prices and figure out what effects those factors are making on the auction price. I then discuss on how to maximize sellers' revenue in OnBid car auctions. The government and public institutes sell their assets through the OnBid auction, hence the optimal design of the OnBid auction is important. The paper's main findings are as follows: (ⅰ) The independent private value model explains OnBid car auction data better than the correlated private value model or the interdependent value model; (ⅱ) Both the number of bidders and the ratios of the auction price to the evaluation value were lower in the auctions posted by the Kamco than auctions by institutes other than the Kamco; (ⅲ) Some auctions require that at least two bidders should submit a bid no less than the reserve price for sale. In those auctions, both the number of bidders and each bidder's valuation on the auctioned object were lower than in auctions without that requirement; (ⅳ) The sum of sellers' revenue would be decreased in the simulation with the reserve price higher by 5%, 10%, and 20% across auctions by institutes other than Kamco.

• Fashion & Textile Research Journal
• /
• v.7 no.5
• /
• pp.509-518
• /
• 2005

In recent years retail competition has intensified, generally as a consequence of technologies, more sophisticated management practices and industry consolidation. An understanding of current customers' loyalty intentions and their determinants is an important basis for the identification of optimal retailer actions. The focus of this study is to examine the links between patronage intention and the effects of various antecedents of current customers' store loyalty intentions in fashion store. 340 female universities students living in Seoul were analyzed by utilizing multiple regressions to investigate the predictability of each of the 4 different sets of variables(consumer value, source of information, clothing benefits, importance of store attributes) on four patronage intentions of apparel shopping(discount store, speciality store, conventional market, Fashion shopping mall). Four factors were significant in predicting conventional market patronage intention. Brand had a negative coefficient, while price, social affiliation, store fashion service/promotion had positive coefficients. Fashion shopping mall were predicted by five factors:brand had a negative coefficient, while media, social affiliation, price, uniqueness had positive coefficients. For specialty store, four factors were significant: brand had a negative coefficient, while store fashion service/promotion, personal sources, uniqueness had positive coefficients. Four factors were significant in predicting discount store patronage intention :price, store fashion service/promotion, social affiliation, variety of price & product had positive coefficients. Despite the relatively low $r^2s$, all four variables appeared to have, to some degree, predictability of choosing among four different types of store for apparel shopping. Based on the results, patronage intention profiles for four retail stores were developed. Marketing implications are discussed.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.67 no.4
• /
• pp.183-190
• /
• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• International Journal of Computer Science & Network Security
• /
• v.22 no.1
• /
• pp.347-357
• /
• 2022

The aim of the study is to model demand for full-time Bachelor's Degree Programs in Hospitality and Catering, taking into account the influence of the main determinants in the COVID-19 pandemic. The research used methods of algorithms, correlation and regression analysis, ANOVA, graphical method, deduction and induction, abstraction, etc. It was found that the demand for full-time Bachelor's Degree Programs in Hospitality and Catering is price elastic. It has been argued that it is useful to consider both price and non-price determinants when modelling demand for full-time Bachelor's Degree Programs in Hospitality and Catering. It is proved that the main determinants of demand for full-time Bachelor's Degree Programs in Hospitality and Catering are full-time tuition fee, maximum government order, license volume and Consolidated Ranking of a higher education institution (HEI). In this case, the applicant decides to enrol in a full-time Bachelor's Degree Program in Hospitality and Catering, guided by the optimal ratio of tuition fee and the prestige of the HEI.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.2
• /
• pp.191-197
• /
• 2020

As an effective means of price discrimination, some suppliers offer trade credit to the distributors for the purpose of increasing the demand of the product they produce. The availability of the delay in payments from the supplier enables discount of the distributor's selling price from a wider range of the price option in anticipation of increased customer's demand. In this regard, we consider the problem of determining the distributor's optimal price and lot size simultaneously when the supplier permits delay in payments for an order of a product whose demand rate is represented by a constant price elasticity function. It is assumed that the distributor pays the shipping cost for the order and hence, the distributor's ordering cost consists of a fixed ordering cost and the shipping cost that depend on the order quantity. For the analysis, it is also assumed that inventory is depleted not only by customer's demand but also by decay. We are able to develop a solution algorithm from the properties of the mathematical model. A numerical example is presented to illustrate the algorithm developed.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.10a
• /
• pp.103-112
• /
• 1996

Optimal pricing research in general has been focused on profit maximizing strategy under the given product life-time T. Here we have tried to study the effect of uncertain product life-time on dynamic optimal pricing strategy. In reality, the life-time of product is more likely to be uncertain and not known as well. In terms of approximating the model to the concerned reality, so-called model validity, it seems to be more desirable to consider the uncertainty of product life-time into the optimal pricing strategy model, For this purpose, we tried two different approaches. One is to consider diverse product life-time probability functions under fixed life-time T. In this case, we might have the same product life-time as the previous study, but the process could be different in the expectation of product's discontinuity. The other is that life-time itself is not determined and thus it is the situation in which we can only decide optimal price on incremental basis. The former is the situation in which although we got some strong guess on life-time of a certain product, the pattern of expected life-time probability could be different. The question is what could be optimal pricing strategies on such different product life-time situations. But since in the latter, we don't assume any idea on the life-time of product. proper optimal pricing could be derived only from the past prices and diffusion information. While the latter seems to be safer in the aspect of model assumption, the former could be more realistic because we might have more or less a prior knowledge on the product life-time itself.

• Management Science and Financial Engineering
• /
• v.14 no.2
• /
• pp.13-23
• /
• 2008

The pricing/lot-sizing problem of determining the robust optimal order quantity and selling price is discussed. The uncertainty of parameters characterized by an ellipsoid is explicitly incorporated into the problem. An approximation scheme is proposed to transform the problem into a geometric program, which can be efficiently and reliably solved using interior-point methods.

• The Pure and Applied Mathematics
• /
• v.19 no.4
• /
• pp.363-381
• /
• 2012

Shortfall risk is considered by taking some exposed risks because the superhedging price is too expensive to be used in practice. Minimizing shortfall risk can be reduced to the problem of finding a randomized test ${\psi}$ in the static problem. The optimization problem can be solved via the classical Neyman-Pearson theory, and can be also explained in terms of hypothesis testing. We introduce the classical Neyman-Pearson lemma expressed in terms of mathematics and see how it is applied to shortfall risk in finance.

• Journal of Korean Institute of Industrial Engineers
• /
• v.21 no.3
• /
• pp.387-396
• /
• 1995

For situations where there are several markets with different profit/cost structures, an economic two-sided screening procedure using a correlated variable is developed. It is assumed that the performance variable and the screening variable are jointly normally distributed. A profit model is constructed which involves selling price, cost incurred by imperfect quality, and screening inspection cost. Methods of finding the optimal screening procedure are presented and numerical examples are given.