• The Journal of Asian Finance, Economics and Business
• /
• 제7권3호
• /
• pp.149-156
• /
• 2020

The study aims to investigate a close relation between macro and non-macro variables on stock performance of tourism companies in Korea. The sample used in this study includes monthly data from January 2001 to December 2018. The stock price index of the tourism companies as a dependent variable are obtained from Sejoong, HanaTour, and RedcapTour as three leading Korean tourism companies that have been listed on the Korea Stock Exchange. This study assesses the tourism stock performance using the quantile regression approach. This study also investigates whether global crisis events as the Iraq War and the global financial crisis as non-macro variables have a significant effect on the stock performance of tourism companies in Korea. The results show that the oil prices, exchange rate and industrial production have negative coefficients on stock prices of tourism companies, while the effects of tourist expenditure and consumer price index are positive and significant. We estimate the result of quantile regression that non-macro determinants have statistically a significant and negative effect on tourism stock performance because the global crisis could threaten traveler's safety and economy. Overall, empirical results suggest that the effects of macro and non-macro variables are statistically asymmetric and highly related to tourism stock performance.

• 한국노년학
• /
• 제31권4호
• /
• pp.855-870
• /
• 2011

본 논문에서는 한국 베이비붐 세대의 미래 사건에 대한 주관적 기대가 자산축적에 미친 효과를 분위회귀분석(quantile regression)을 통해 살펴보았다. 한국고령화패널 1, 2차년도(2006, 2008년) 자료를 이용하여 자산 결정요인을 추정한 결과, 학력, 성별, 자녀수는 어떤 분위에서도 유의한 결정요인이었다. 특히, 학력과 자녀수는 상위 분위로 갈수록 총자산의 격차를 확대시키는 방향으로 증가하였다. 미래 사건에 대한 주관적 기대의 경우, 증여에 대한 기대가 높을수록, 좀 더 긴 수명을 기대할수록, 베이비붐 세대는 좀 더 많은 자산축적을 하고 있는 것으로 추정되었다. 한편 노후 생활에 대한 국가의 보장 정도가 개인의 자산축적을 구축하는 정도는 총자산 하위 분위 보다는 상위 분위에서 증가하였으며, 상위 분위의 효과는 통계적으로도 유의하였다.

• 치위생과학회지
• /
• 제19권4호
• /
• pp.271-278
• /
• 2019

Background: This study aimed to examine the factors that influence clinical performance of dental hygiene students to provide useful data for developing strategies to improve clinical competence. Methods: The effects of variables on clinical competence by quantile level were analyzed using quantile regression analysis in 247 dental hygiene students. Quantile regression and multiple regression analyses were conducted using the Stata 11.0 program to analyze predictors of clinical competence. Results: The clinical competence score of dental hygiene students was 42.69±5.90, the satisfaction of clinical practice was 49.90±7.44, the clinical practice stress was 50.62±7.37, and the professional self-concept was 31.68±4.41. Empathy was the highest at 50.87±4.93. Multiple regression analysis showed that school year, stress from clinical training, satisfaction with clinical training, professional self-concept, and empathy had significant impact on clinical competence. Quantile regression analysis showed that the effects varied depending on the clinical competence level. School year and professional self-concept had a significant positive effect, regardless of the clinical competence level, while empathy had a significant positive effect at the top 10% (Q90) of the clinical competence level. Satisfaction with clinical practice affected clinical competence at Q25, Q50, and Q90. Stress from clinical practice had significant effects at Q25, Q50, and Q90 (p<0.05). Conclusion: According to the study results, different factors affected clinical competence according to the quantile of clinical competence. This study provides valuable implications for designing clinical competence enhancement programs and strategies. In addition, objective indicators for considering factors that may affect the clinical competence, such as academic competence and satisfaction of practice hospitals, are expected to require detailed analysis and measures.

• Communications for Statistical Applications and Methods
• /
• 제15권5호
• /
• pp.753-764
• /
• 2008

The $L_1$ regularized estimator in quantile problems conduct parameter estimation and model selection simultaneously and have been shown to enjoy nice performance. However, $L_1$ regularized estimator has a drawback: when there are several highly correlated variables, it tends to pick only a few of them. To make up for it, the proposed method adopts doubly regularized framework with the mixture of $L_1$ and $L_2$ norms. As a result, the proposed method can select significant variables and encourage the highly correlated variables to be selected together. One of the most appealing features of the new algorithm is to construct the entire solution path of doubly regularized quantile estimator. From simulations and real data analysis, we investigate its performance.

• 한국조사연구학회:학술대회논문집
• /
• 한국조사연구학회 2006년도 추계학술대회 발표논문집
• /
• pp.67-83
• /
• 2006

In successive sampling on two occasions the problem of estimating a finite population quantile has been considered. The theory developed aims at providing the optimum estimates by combining (i) three double sampling estimators viz. ratio-type, product-type and regression-type, from the matched portion of the sample and (ii) a simple quantile based on a random sample from the unmatched portion of the sample on the second occasion. The approximate variance formulae of the suggested estimators have been obtained. Optimal matching fraction is discussed. A simulation study is carried out in order to compare the three estimators and direct estimator. It is found that the performance of the regression-type estimator is the best among all the estimators discussed here.

• Journal of the Korean Statistical Society
• /
• 제36권4호
• /
• pp.543-556
• /
• 2007

In successive sampling on two occasions the problem of estimating a finite population quantile has been considered. The theory developed aims at providing the optimum estimates by combining (i) three double sampling estimators viz. ratio-type, product-type and regression-type, from the matched portion of the sample and (ii) a simple quantile based on a random sample from the unmatched portion of the sample on the second occasion. The approximate variance formulae of the suggested estimators have been obtained. Optimal matching fraction is discussed. A simulation study is carried out in order to compare the three estimators and direct estimator. It is found that the performance of the regression-type estimator is the best among all the estimators discussed here.

• 산업경영시스템학회지
• /
• 제43권4호
• /
• pp.107-115
• /
• 2020

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the ⲉ-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within ⲉ. In most studies, the ⲉ-insensitive loss function is used symmetrically, and it is of interest to determine the value of ⲉ. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of ⲉ and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of ⲉ. In this study, a new ε-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of ⲉ and the slope of the penalty using the parameters p1 and p2, respectively. Moreover, the figures show that the asymmetry of the width of ⲉ and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.

• 한국수자원학회논문집
• /
• 제45권8호
• /
• pp.815-826
• /
• 2012

기존 Ordinary Regression (OR) 방법을 이용한 경향성 분석은 경향성을 과소평가하는 문제점을 나타낸다. 이러한 점에서 본 연구에서는 자료의 정규분포 가정과 평균을 중심으로 경향성 평가가 이루어지는 기존 Ordinary Regression (OR) 방법을 개선한 Quantile Regression (QR) 방법을 제안하였다. 본 연구에서는 64개 강우 관측지점의 연 최대 극대강수량 자료에 대하여 QR 방법과 OR 방법에 대하여 통계적 성능을 평가하였다. QR 방법의경향성 분석결과 47개 지점에서 5% 오차수준 내에서 t-검정을 통과한 반면 OR 방법에서는 13개 지점 만이 통계적 유의성을 가지는 것으로 나타났다. 이는 OR 방법이 자료의 평균을 중심으로 경향성을 평가하는 기법인데 반해 QR은 자료의 다양한 분위에서 경향성을 평가함으로써 극대 및 극소 부분에서의 경향성을 보다 유연하게 감지하는 이유로 판단된다. QR 방법을 통한 경향성 평가는 평균 중심의 해석문제점을 개선할 수 있으며 자료가 정규분포를 따르지 않거나 왜곡된 분포형태를 갖는 자료의 수문학적 경향성 평가에 유용하게 사용될 수 있을 것으로 판단된다.

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2000년도 추계학술발표회 논문집
• /
• pp.187-191
• /
• 2000

In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. In this paper, we propose a simple graphical method to detect outliers and to investigate the goodness of model fit in repeated measures data. The graphical method is based on the quantile-quantile(Q-Q) plots of the $X^2$ distribution and the standard normal distribution. We also propose diagnostic measures to detect influential observations. The proposed method is illustrated using two examples.

• 응용통계연구
• /
• 제25권2호
• /
• pp.321-330
• /
• 2012

In this paper we study some basic statistics in quantile regression. In particular, we investigate the residual, goodness-of-fit statistic and the effect of one or few observations on estimates of regression coefficients. In addition, we compare the proposed goodness-of-fit statistic with the statistic considered by Koenker and Machado (1999). An illustrative example based on real data sets is given to see the numerical performance of the proposed basic statistics.