• Communications for Statistical Applications and Methods
• /
• v.22 no.5
• /
• pp.487-494
• /
• 2015

A calculation of the Kullback-Leibler information of consecutive order statistics is complicated because it depends on a multi-dimensional integral. Park (2014) discussed a representation of the Kullback-Leibler information of the first r order statistics in terms of the hazard function and simplified the r-fold integral to a single integral. In this paper, we first express the Kullback-Leibler information in terms of the reversed hazard function. Then we establish a generalized result of Park (2014) to an arbitrary consecutive order statistics. We derive a single integral form of the Kullback-Leibler information of an arbitrary block of order statistics; in addition, its relation to the Fisher information of order statistics is discussed with numerical examples provided.

• Fashion, Industry and Education
• /
• v.16 no.1
• /
• pp.19-35
• /
• 2018

This study explores the effect of information direction and order of cosmetics power bloggers on consumer responses. A total of 488 undergraduate students participated in experiments with mock-up stimuli of sunscreen product reviews by power bloggers. The study was conducted with four stimuli of product review posts (i.e., positive reviews only, positive-negative reviews in order, negative-positive reviews in order, negative reviews only) of the power bloggers. The results showed a significant difference in consumer responses according to information direction and order of product reviews of the power bloggers. Specifically, negative reviews were considered more objective and more useful than positive reviews were. However, positivity of reviews is crucial in generating more positive attitudes toward products, greater purchase intention, and greater word-of-mouth intention. In regard to information order, the negative-positive reviews generated more positive attitudes toward the product and greater purchase intention than did the positive-negative reviews, emphasizing the importance of ending product reviews with positive information so as to create positive responses. Referring to the findings, power bloggers and marketers using bloggers as a promotional tool would benefit by carefully designing information content in consideration of an appropriate direction and order of information to better fit their purpose.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.89-92
• /
• 2002

As the use of Internet being generalized, ordering system can be easily achieved on the Web so that the direct sailing system is replaced the various types of Built-To-Order system. It is important to give the up-to-date information to the end-user in order to build the Built-To-Order system. Recently, the way of providing information is updating the product related information continuously. In this study, we provide the method that can be streamlined Product Data Management System which manages a creation and update for product information into the direct BTO system in order to achieve the newest information. This methodology is not only transferring the information but also processing the information in an appropriate form. So this paper is going to cover the various point of view of information processing.

• IE interfaces
• /
• v.18 no.2
• /
• pp.148-157
• /
• 2005

The manufacturing industry with Make-to-Order production system is difficult to decide the standard information for the product and the demand is variable to estimate. In this paper, we concerned with the process planning method using data mining in the manufacturing industry with Make-to-Order environment. The subject of our study is the industry transformer plant which is received an diverse order of customer and then produced the product. Currently, process planning method is classified the standard information by hand based on the acquired knowledge through the experience. The standard information stored the various information, such as work sequence, time and so on. This process planning method needs an experts which possesses the field experience for several years. For the product specification which is varied in each order, current process planning method is not efficient due to need many times To solve this problem, we extract the information using data mining process for each processing time, and then construct the knowledge base. We propose a method which is the process planning of the industry transformer product in Make-to-Order environment using the knowledge base.

• International Journal of Highway Engineering
• /
• v.15 no.1
• /
• pp.135-141
• /
• 2013

PURPOSES: In this study, as part of an effort to develop HUD for public transit, it is proposed that the decision of order priority of contents which will be disposed to bus drivers through HUD for public transit using AHP(Analytic Hierarchy Process) technique. METHODS: In AHP analysis method brainstorming, factor analysis, hierarchical structuring, and weighting analysis were performed by applying a classical analysis method. RESULTS: By the result of analysis it is shown that unlike car drivers, bus drivers prefer information related to bus intervals, bus stop, and door open and close to information related to vehicle running. Also, bus stop information and bus interval information were ranked as first and second place in order priority of HUD contents for public transit by experts. CONCLUSIONS: This method of selecting order priority of HUD contents for public transit can provide a basic foundation for selecting order priority of traffic information contents as well as other HUD contents.

• Communications for Statistical Applications and Methods
• /
• v.6 no.2
• /
• pp.397-402
• /
• 1999

We first derive the Fisher information identity in order statistics in terms of the hazard rate by considering the Fisher information identity in terms of the hazard rate (Efron and Johnstone, 1990). Then we use the identity and show an interesting and useful result that some identities and recurrence relations for the Fisher information in order statistics can be directly obtained from those between the c.d.f.s of order statistics.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.157-177
• /
• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.636-647
• /
• 2004

The objective of this paper is to provide an improved reorder decision policy for general multi-echelon distribution systems utilizing the shared stock information. It has been known that traditional reorder policies sometimes show poor performance in distribution systems. Thus, in our previous research we introduced the order risk policy which utilizes the shared stock information more accurately for the 2-echelon distribution system and proved the optimality. However, since the real world supply chain is generally composed with more than 2 echelons, we extend the order risk policy for the general multi-echelon systems. Since the calculation of the exact order risk value for general multi-echelon systems is very complex, we provide two approximation methods for the real-time calculation. Through the computational experiment comparing the order risk policy with the existing policies under various conditions, we show the performance of the order risk policy and analyze the value of the shared stock information varying with the characteristics of the supply chain.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.11a
• /
• pp.159-165
• /
• 2003

The objective of this paper is to provide an improved reorder decision policy for general multi-echelon distribution systems utilizing the shared stock information. Since it has been known that traditional reorder policies sometimes show poor performance in distribution systems, in our previous research we introduced the order risk policy which utilizes the shared stock information more accurately f3r the 2-echelon distribution system and proved the optimality. However, since the real world supply chain is generally composed with more than 2 echelons, we extend the order risk policy for the general multi-echelon systems. Since the calculation of the exact order risk value fur general multi-echelon systems is very complex, we provide two approximation methods. Through the computational experiment comparing the order risk policy with the existing policies under various conditions, we show the performance of the order risk policy and analyze the value of the shared stock information varying with the characteristics of the supply chain.

• IE interfaces
• /
• v.17 no.3
• /
• pp.359-374
• /
• 2004

The objective of this paper is to provide an improved reorder decision policy for general multi-echelon distribution systems utilizing the shared stock information. It has been known that traditional reorder policies sometimes show poor performance in distribution systems. Thus, in our previous research we introduced the order risk policy which utilizes the shared stock information more accurately for the 2- echelon distribution system and proved the optimality. However, since the real world supply chain is generally composed with more than 2 echelons, we extend the order risk policy for the general multi-echelon systems. Since the calculation of the exact order risk value for general multi-echelon systems is very complex, we provide two approximation methods for the real-time calculation. Through the computational experiment comparing the order risk policy with the existing policies under various conditions, we show the performance of the order risk policy and analyze the value of the shared stock information varying with the characteristics of the supply chain.