• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.71-87
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• 2014

The Bayes premium or the net premium in the credibility theory does not reflect the underlying tail risk. In this study we examine how the tail risk measures can be utilized in determining the risk premium. First, we show that the risk measures can not only provide the proper risk loading, but also allow the insurer to avoid the wrong decision made with the Bayesian premium alone. Second, it is illustrated that the rank of the tail thickness among different conditional loss distributions does not preserve for the corresponding predictive distributions, even if they share the identical prior variable. The implication of this result is that the risk loading for a contract should be based on the risk measure of the predictive loss distribution not the conditional one.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.533-545
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• 2017

Quantile regression models provide a variety of useful statistical information by estimating the conditional quantile function of the response variable. However, the traditional linear quantile regression model can lead to the distorted and incorrect results when analysing real data having a nonlinear relationship between the explanatory variables and the response variables. Furthermore, as the complexity of the data increases, it is required to analyse multiple response variables simultaneously with more sophisticated interpretations. For such reasons, we propose a multivariate quantile regression tree model. In this paper, a new split variable selection algorithm is suggested for a multivariate regression tree model. This algorithm can select the split variable more accurately than the previous method without significant selection bias. We investigate the performance of our proposed method with both simulation and real data studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.393-396
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• 2009

This paper describes a design of variable-length FFT/IFFT processor for OFDM-based communication systems. The designed FFT/IFFT processor adopts the in-place single-memory architecture, and uses a hybrid structure of radix-4 and radix-2 DIF algorithms to accommodate FFT lengths of $N=64{\times}2^k$ ($0{\leq}k{\leq}7$). To achieve both memory size reduction and the improved SQNR, a two-step conditional scaling technique is devised, which conditionally scales the intermediate results of each computational stage. The performance analysis results show that the average SQNR's of 64~8,192-point FFT's are over 60-dB. The processor synthesized with a $0.35-{\mu}m$ CMOS cell library can operate with 75-MHz@3.3-V clock, and 64-point and 8,192-point FFT's can be computed in $2.55-{\mu}s$ and $762.7-{\mu}s$, respectively, thus it satisfies the specifications of wireless LAN, DMB, and DVB systems.

• Journal of Distribution Science
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• v.12 no.4
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• pp.73-83
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• 2014

Purpose - This study aims to classify and extend the consumer value of movie theaters into various values such as functional value, emotional value, social value, epistemic value, and conditional value based on the theory of consumption value by Sheth, Newman and Gross (1991). It also aims to verify the path structure of consumption value→customer satisfaction→behavior intention of movie theaters to confirm its generalization. Research design, data, and methodology - This study was conducted by collecting data on Kimpo university students from various areas in Incheon, Northern Seoul, Ilsan, Kyonggi Province, and Kimpo City. The survey was conducted by distributing 280 survey papers from Oct. 5 to 15, 2013 and collecting 238 of them. The final analysis used 208 questionnaires, after excluding 30 invalid responses. The statistical analysis of this study used the SPSS 19.0 statistics package. Results - The results of the survey are as follows: First, consumption values of movie theaters are classified into the following five groups: functional value, emotional value, social value, epistemic value, and conditional value. This study verified that consumption values play a role as a previous variable of customer satisfaction. Second, functional value, emotional value, and epistemic value have positive effects on customer satisfaction. On the other hand, social value and conditional value do not affect customer satisfaction. Finally, customer satisfaction has a positive impact on behavior intention. Theater users have an intention to re-use or recommend the movie theater they used when they are satisfied with a movie theater's physical environment and services. Conclusions - This study can provide academic and practical implications as follows based on the results mentioned above. First, academic implications can be found in that consumption values of movie theater users are classified into five values based on the theory of consumption value by Sheth et al. (1991). In the previous study, the service quality of a movie theater was studied based on the service quality of service encounters and a physical environment→customer satisfaction→behavior intention path structure. However, this study was verified by a consumption value→customer satisfaction→behavior intention path structure to classify consumption value, but not service quality or perceived value of quality, to confirm this generalization. Second, practical implications can be found in that the relative impact of consumption value of movie theaters on consumer satisfaction showed that functional value was followed by epistemic value and emotional value. In the previous study on movie theaters, previous variables of customer satisfaction were separated only by functional service quality including service encounters and physical environment; in some other studies, quality of service encounter had a direct effect on customer satisfaction. Accordingly, a marketing manager of a movie theater should develop various differentiated services by reflecting not only functional value such as service encounters and physical environment but also epistemic value and emotional value.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.179-190
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• 2020

This research analyses the financial characteristics of corporate R&D intensity in the Korean capital market. It is important to pay greater attention to this subject, given the current situation of the shortage of core components domestically in Korea. Three hypotheses are postulated to investigate the financial factors of R&D investments for KOSDAQ-listed firms during the post-era of the global financial turmoil. By applying a conditional quantile regression (CQR) model, three variables included R&D intensity in the previous year (Lag_RD), the squared term of Lag_RD, and interaction between the high-tech sector and Lag_Rd, reveal significant effects on the current R&D ratio. Whereas more than half of the total variables show variable impacts between firms with higher and lower R&D intensity, only Lag_RD and squared term of Lag_RD were found to be significant. It is expected that these results may contribute to being financial catalysts for an optimal level of R&D expenditures, thereby maximizing firm value for shareholders in KOSDAQ-listed firms.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2A
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• pp.209-215
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• 2010

This paper describes a design of variable-length FFT/IFFT processor (VL_FCore) for OFDM-based multi-standard communication systems. The VL_FCore adopts in-place single-memory architecture, and uses a hybrid structure of radix-4 and radix-2 DIF algorithms to accommodate various FFT lengths in the range of $N=64{\times}2^k\;(0{\leq}k{\leq}7)$. To achieve both memory size reduction and the improved SQNR, a two-step conditional scaling technique is devised, which conditionally scales the intermediate results of each computational stage. The performance analysis results show that the average SQNR's of 64~8,192-point FFT's are over 60-dB. The VL_FCore synthesized with a $0.35-{\mu}m$ CMOS cell library has 23,000 gates and 32 Kbytes memory, and it can operate with 75-MHz@3.3-V clock. The 64-point and 8,192-point FFT's can be computed in $2.25-{\mu}s$ and $762.7-{\mu}s$, respectively, thus it satisfies the specifications of various OFDM-based systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.537-552
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• 2019

Purpose: The development of information technology makes it easy to utilize high-dimensional categorical data. In this regard, the purpose of this study is to propose a novel method to select the proper categorical variables in high-dimensional categorical data. Methods: The proposed feature selection method consists of three steps: (1) The first step defines the goodness-to-pick measure. In this paper, a categorical variable is relevant if it has relationships among other variables. According to the above definition of relevant variables, the goodness-to-pick measure calculates the normalized conditional entropy with other variables. (2) The second step finds the relevant feature subset from the original variables set. This step decides whether a variable is relevant or not. (3) The third step eliminates redundancy variables from the relevant feature subset. Results: Our experimental results showed that the proposed feature selection method generally yielded better classification performance than without feature selection in high-dimensional categorical data, especially as the number of irrelevant categorical variables increase. Besides, as the number of irrelevant categorical variables that have imbalanced categorical values is increasing, the difference in accuracy between the proposed method and the existing methods being compared increases. Conclusion: According to experimental results, we confirmed that the proposed method makes it possible to consistently produce high classification accuracy rates in high-dimensional categorical data. Therefore, the proposed method is promising to be used effectively in high-dimensional situation.

• Journal of Korean Academy of Dental Administration
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• v.11 no.1
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• pp.78-88
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• 2023

This study aimed to analyze the correlation between working from home and sleep disorders among domestic workers using data from the 5th Working Environment Survey in 2017. Out of the total 30,108 wage workers, 818 employees work from home and 4,090 work in an office. A random sample of 1:5 pairs, considering gender and occupational group, was selected from these employees as the study subjects. The analysis included personal characteristics, occupational characteristics, work-from-home arrangements, and sleep disorders. Age, education, employment status, years in the workforce, weekly working hours, work-life balance, self-perceived health, depression, and anxiety were all adjusted as potential confounding variables. Conditional logistic regression analysis was conducted to examine the relationship between working from home (independent variable) and sleep disorder (dependent variable). This analysis aimed to analyze the correlation between working from home and sleep disorder. The analysis revealed that working from home was associated with sleep onset latency disorder OR=3.23 (95% CI=2.67~3.91), sleep maintenance disorder OR=3.67 (95% CI=3.02~4.45), and non-restorative sleep OR=3.01 (95% CI=2.46~3.67), which showed a statistically significant relationship with all three types of sleep disorders. Factors influencing the correlation between working from home and sleep disorders included work-life balance, social isolation, and anxiety.