• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.185-195
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• 2022

Purpose - This study attempts to analyze the determinants of inventory turnover by applying quantile regression analysis. Design/methodology/approach - By selecting the gross margin, capital intensity, and sale surprise as the determinants of inventory turnover, we investigate their effects on inventory turnover at the several quartiles (10%, 25%, 50%, 75%, 90%) of inventory turnover with quantile regression analysis. Findings - The effects of gross margin and capital intensity on inventory turnover are different for each quartile. But the effects of sale surprise on inventory turnover are not different for each quartile. Research implications or Originality -This study is the first attempt to examine the effects of inventory turnover determinants on inventory turnover by applying quantile regression analysis was not employed in the prior studies. Thus, this study is meaningful in that it shows the possible way to review inventory management strategies that can be applied differently to the firms with different inventory turnover levels.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.443-452
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• 2018

The purpose of this study was to investigate the utilization behavior of medical services according to the characteristics of socioeconomic status (SES) and prevalence by using the 6th National Health and Nutrition Survey data for adults over 25 years old. Frequency and technical statistical analysis, ANOVA, ${\chi}^2$-test, and regression analysis were performed using SPSS 23.0. The results were as follows: more female than male, 65 years old and over, high school graduate, and unemployed and income quintiles were similar. The SES score considering education level, function, and income quintiles was the highest at 7-8, and most of the respondents felt moderate about their subjective health condition. The mean number of prevalence was $1.07{\pm}1.497$, the mean utilization of outpatient services was $0.50{\pm}0.045$, and the mean number of inpatient services use was $0.12{\pm}0.454$. Depending on general characteristics, there was a significant difference between subjects with prevalence and subjective health conditions. Higher age was associated with lower education, skill level, income, and SES score, and average prevalence was associated with poor subjective health conditions. More serious prevalence was associated with greater utilization of inpatient services. More chronic prevalence was associated with utilization of outpatient services. In other words, higher SES score was associated with lower overall use of medical services. Lower SES score was associated with higher use of medical services. In conclusion, we must develop appropriate health education programs that can prevent diseases in groups with low socioeconomic characteristics. There is the need to construct and implement a community-based appropriate health service system so that proper medical services can be used.

• Information Systems Review
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• v.24 no.2
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• pp.1-20
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• 2022

Researchers are making contradictory claims through the concept of superstars and long tails about how the development of IT technology affects demand distribution. Unlike previous studies that focused on changes in demand from a macro point of view, this study explored whether the relationship between a company's marketing activities and consumer response differs depending on the product location (i.e., superstar vs. long tail) from a micro point of view. Based on the marketing mix framework, hypotheses were developed based on the relevant literature. In the case of empirical analysis, 2,835 daily data from 63 Korean films were tested using the quantile regression method. As a result of the analysis, it was found that the influence of marketing mix factors on sales varies depending on the location of the product. Specifically, the appeal breadth of the film and the effect of owned media are enhanced in superstar products, and the effect of acquisition media in long-tail products is enhanced and the negative effects of competition are mitigated. Unlike previous studies that focused on macroscopic changes in demand distribution, this study suggested marketing activities suitable for practitioners through microscopic analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.182-182
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• 2021

본 연구에서는 1일부터 최대 7일까지의 시간을 두고 남한 전체의 유출량에 대한 예측 모형을 제시하고자 한다. 이를 위하여 LSM (Land Surface Model) 모형을 사용하여 유출량을 모의하였고 이 과정에서 미 계측치에 대한 유출량을 예측하기 위하여 Xgboost (Extreme Gradient Boost)를 활용하여 매개변수를 지역화하였다. 이러한 지역화 기법을 통하여 남한 전체의 유출량에 대한 그리드화 된 유출값을 얻을 수 있었다. 또한 본 연구에서는 기상 예측자료를 유출량에 대한 예측으로 변환하기 위하여 Stacking 앙상블 기반의 수문학적 후처리 기법을 사용하였다. Stacking 앙상블 기법은 Base-learner와 Meta-learner의 조합으로 이루어 지는데 본 연구에서 새롭게 사용되는 패널티 기반의 분위회귀분석 방법론은 기존의 방법론과의 비교에 있어서 유용한 것으로 파악되었다. 결과적으로 본 연구에서는 총 7일의 앞선 시간의 예측에 있어서 한반도 전체의 유출량에서 비교적 짧은 시간에 대한 예측인 1일과 2일에서의 예측은 실질적으로 사용이 가능한 것으로 파악되었다.

• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.153-164
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• 2022

In this study, we estimate the quantile-regression framework of the shipping industry for the Capesize used ship, which is a typical raw material transportation from January 2000 to December 2021. This research aims two main contributions. First, we analyze the relationship between the Capesize used ship, which is a typical type in the raw material transportation market, and the freight market, for which mixed empirical analysis results are presented. Second, we present an empirical analysis model that considers the structural transformation proposed in the Hyunsok Kim and Myung-hee Chang(2020a) study in quantile-regression. In structural change investigations, the empirical results confirm that the quantile model is able to overcome the problems caused by non-stationarity in time series analysis. Then, the long-run relationship of the co-integration framework divided into long and short-run effects of exogenous variables, and this is extended to a prediction model subdivided by quantile. The results are the basis for extending the analysis based on the shipping theory to artificial intelligence and machine learning approaches.

• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.755-775
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• 2022

This study empirically analyzes that the asymmetry of domestic gasoline price adjustment to the crude oil price changes can vary depending on the level of gasoline price using quantile autoregressive distributed lag model. The data used are the weekly average Dubai price, domestic gasoline price at refiners and gas stations from the first week of May 2008 to the second week of October 2022. The study estimates three price transmission channels: changes in gas station gasoline prices in response to changes in Dubai oil prices, changes in refiners gasoline prices in response to changes in Dubai oil prices, and changes in gas station prices relative to refiners gasoline prices. As a result, the price adjustment of refiner's gasoline price with respect to Dubai oil price appears asymmetrically across all quantiles of gasoline price, whereas the adjustment of gas station prices for Dubai oil price and refiner's gasoline price tend to be more asymmetric as the quantile of gasoline price increases. Such a result is presumed to be due to changes in the inventory cost of gas stations. When the burden of inventory cost is high, gas stations have an incentive to more actively pass the increased buying price on their selling price.

• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.1159-1191
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• 2017

This study considered technology commercialization as a sort of external R&D of the licensee firm. Then, this study analyzed industrial characteristics of technology commercialization and interactions between internal R&D and technology commercialization from the licensee's viewpoint. Data from NTIS (National science and Technology Information Service) and KED (Korea Enterprise Database) were matched. 7,645 technology commercializations from 1,980 firms were extracted. Afterward, OLS and quantile regression were applied to the extracted data. The impact of technology commercialization on firm growth was concentrated to few high-tech and medium high-tech firms. Technology commercialization was effective in the growth in a year while internal R&D was effective in the growth in two years. The firm size was insiginificant variable. In analysis of 4 selected industries (automobile, electronics, semiconductor, chemistry), the impact was skewed among industries. Though the importance of technology commercialization is widely acknowledged, quantitative analyses like this study are uncommon. Therefore, this study can be useful for the tailored industry solutions for technology commercialization.

• Korean Journal of Adult Nursing
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• v.27 no.3
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• pp.273-282
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• 2015

Purpose: The objective of this study was to identify the predictors of self-care behaviors among elderly patients with hypertension using quantile regression method. Methods: A total of 253 elderly patients diagnosed with hypertension was recruited via 3 different medical clinics for the study. The quantile regression and a liner regression was conducted using Stata 12.0 program by analyzing predictors of self-care behaviors. Results: In the ordinary least square, self-efficacy, period of disease, and education level explained 42% of the variance in self-care activities. In the quantile regression, affecting predictors of self-care behaviors were self-efficacy for all quantiles, the period of disease for from 60% quantile to 90% quantile, education level for 20%, 30%, and 50% quantiles, economic status for 10%, 50%, and 60% quantiles, age for 10%, 70% quantiles, fatigue for 10% quantile, knowledge about hypertension for 10% and 20% quantiles, and depression for 30% and 40% quantiles. Conclusion: The affecting predictors of self-care behaviors among elderly with hypertension were different from the level of self-care behaviors. These results indicated the significance in assessing predictors according to the level of self-care behaviors when clinical nurses examine the patients' health behaviors and plan any intervention strategies. Specially, education level and knowledge about hypertension were the significant predictors of self-care activities for low quantiles. Clinical nurses may promote self-care activities of the given population though health education programs.

• Journal of Coastal Disaster Prevention
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• v.4 no.3
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• pp.119-131
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• 2017

Global warming under the influence of climate change and its direct impact on glacial and sea level are known issue. However, there is a lack of research on an indirect impact of climate change such as coastal structure design which is mainly based on a frequency analysis of water level under the stationary assumption, meaning that maximum sea level will not vary significantly over time. In general, stationary assumption does not hold and may not be valid under a changing climate. Therefore, this study aims to develop a novel approach to explore possible distributional changes in annual maximum sea levels (AMSLs) and provide the estimate of design water level for coastal structures using a multiple non-crossing quantile regression based nonstationary frequency analysis within a Bayesian framework. In this study, 20 tide gauge stations, where more than 30 years of hourly records are available, are considered. First, the possible distributional changes in the AMSLs are explored, focusing on the change in the scale and location parameter of the probability distributions. The most of the AMSLs are found to be upward-convergent/divergent pattern in the distribution, and the significance test on distributional changes is then performed. In this study, we confirm that a stationary assumption under the current climate characteristic may lead to underestimation of the design sea level, which results in increase in the failure risk in coastal structures. A detailed discussion on the role of the distribution changes for design water level is provided.