• Smart Structures and Systems
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• v.20 no.1
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• pp.11-22
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• 2017

The long-term effect of ambient temperature on bridge strain is an important and challenging problem. To investigate this issue, one year data of strain and ambient temperature of a long-span cable-stayed bridge is studied in this paper. The measured strain-time history is decomposed into two parts to obtain the strains due to vehicle load and temperature alone. A linear regression model between the temperature and the strain due to temperature is established. It is shown that for every $1^{\circ}C$ increase in temperature, the stress is increased by 0.148 MPa. Furthmore, the extreme value distributions of the strains due to vehicle load, temperature and the combination effect of them during the remaining service period are estimated by the average conditional exceedance rate approach. This approach avoids the problem of declustering of data to ensure independence. The estimated results demonstrate that the 95% quantile of the extreme strain distribution due to temperature is up to $1.488{\times}10^{-4}$ which is 2.38 times larger than that due to vehicle load. The study also indicates that the estimated extreme strain can reflect the long-term effect of temperature on bridge strain state, which has reference significance for the reliability estimation and safety assessment.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.627-641
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• 2021

An asymmetric least squares estimation method has been employed to estimate linear models for percentile regression. An asymmetric maximum likelihood estimation (AMLE) has been developed for the estimation of Poisson percentile linear models. In this study, we propose generalized nonlinear percentile regression using the AMLE, and the use of the parametric bootstrap method to obtain confidence intervals for the estimates of parameters of interest and smoothing functions of estimates. We consider three conditional distributions of response variables given covariates such as normal, exponential, and Poisson for three mean functions with one linear and two nonlinear models in the simulation studies. The proposed method provides reasonable estimates and confidence interval estimates of parameters, and comparable Monte Carlo asymptotic performance along with the sample size and quantiles. We illustrate applications of the proposed method using real-life data from chemical and radiation epidemiological studies.

• Korea Real Estate Review
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• v.28 no.1
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• pp.79-90
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• 2018

The purpose of this study was to estimate the pricing factors of residential lands in new cities by estimating the pricing model of residential lands. For this purpose, hedonic equations for each quantile of the conditional distribution of land prices were estimated using quantile regression methods and the sale price date of Jangyu New Town in Gimhae. In this study, a quantile regression method that models the relation between a set of explanatory variables and each quantile of land price was adopted. As a result, the differences in the effects of the characteristics by price quantile were confirmed. The number of years that elapsed after the completion of land construction is the quadratic effect in the model because its impact may give rise to a non-linear price pattern. Age appears to decrease the price until certain years after the construction, and increases the price afterward. In the estimation of the quantile regression, land age appears to have a statistically significant impact on land price at the traditional level, and the turning point appears to be shorter for the low quantiles than for the higher quantiles. The positive effects of the use of land for commercial and residential purposes were found to be the biggest. Land demand is preferred if there are more than two roads on the ground. In this case, the amount of sunshine will improve. It appears that the shape of a square wave is preferred to a free-looking land. This is because the square land is favorable for development. The variables of the land used for commercial and residential purposes have a greater impact on low-priced residential lands. This is because such lands tend to be mostly used for rental housing and have different characteristics from residential houses. Residential land prices have different characteristics depending on the price level, and it is necessary to consider this in the evaluation of the collateral value and the drafting of real estate policy.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.729-739
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• 2016

The primary objective of implementing the study was to further investigate any pronounced financial components affecting the level of cash retention for the Korean chaebol firms. The research was framed to test for two hypotheses on the cash savings with utilizing the chaebol firms during the post-era of the global financial turmoil (from 2009 to 2013). In the first hypothesis test, any significant explanatory variables relative to the cash holdings, were identified in each corresponding category of the conditional quantile regression (CQR) model, while multilogistic regression analysis was performed to discriminate relevant financial factors in each pair of classes consisting of the chaebol firms. Concerning the results, liquidity, agency costs, and cash conversion cycle were found to be statistically significant in the majority of classified categories in the former test and liquidy, firm size, and dividend yield, also showed discriminating powers in each pair of categorical for the firms in the latter test.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.119-130
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• 2017

In many literatures on VaR and CTE for multivariate distribution, these are estimated by using transformed univariate distribution with a specific ratio of many kinds of portfolios. Even though there are lots of works to define quantiles for multivariate distributions, there does not exist a quantile uniquely. Hence, it is not easy to define the VaR and CTE. In this paper, we propose the weighted CTE vectors corresponding to various ratio combinations of many kinds of portfolios by extending the researches on the alternative VaR and integrated multivariate CTE based on multivariate quantiles. We extend relation equations about univariate CTEs to multivariate CTE vectors and discuss their characteristics. The proposed weighted CTEs are explored with some data from multivariate normal distribution and illustrative examples.

• Journal of IKEEE
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• v.5 no.1 s.8
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• pp.1-7
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• 2001

In this paper, we have investigated the CDMA(Code Division Multiple Access) Cellular System with non-linear equalizer in reverse link channel. In general, due to unknown characteristics of channel in the wireless communication, the distribution of the observables cannot be specified by a finite set of parameters; instead, we partitioned the m-dimensional sample space Into a finite number of disjointed regions by using quantiles and a vector quantizer based on training samples. The algorithm proposed is based on a piecewise approximation to regression function based on quantiles and conditional partition moments which are estimated by Robbins Monro Stochastic Approximation (RMSA) algorithm. The resulting equalizers and detectors are robust in the sense that they are insensitive to variations in noise distributions. The main idea is that the robust equalizers and robust partition detectors yield better performance in equiprobably partitioned subspace of observations than the conventional equalizer in unpartitioned observation space under any condition. And also, we apply this idea to the CDMA system and analyze the BER performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.23-23
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• 2018

본 연구에서는 다중분위회귀분석모형(Multiple Quantile Regression Model, MQRM)과 MODIS(MODerate resolution Imaging Spectroradiometer) LST (Land Surface Temperature) 자료를 이용하여 전국 공간토양수분을 산정하였다. 공간토양수분을 산정하기 위한 과정은 크게 두가지로 구분된다. 첫 번째로 기존의 MODIS LST 자료를 조건부 합성 보정기법을 적용하여 실측 LST 자료와 비교하여 위성 LST 자료가 갖고 있는 오차를 보정하였다. 그 결과, 조건부 합성 보정기법을 적용하기전 전국 71개 지상관측지점에서 관측한 실측 LST와 MODIS LST의 $R^2$는 전체 평균 0.70으로 어는정도 유의성 있는 상관관계를 나타냈으나 조건부 합성 보정기법을 적용한 후 실측 LST와 MODIS LST의 $R^2$는 전체 평균 0.92로 상당히 크게 향상됨을 알 수 있었다. 두 번째로 보정된 MODIS LST를 이용하여 다중분위회귀분석 모형을 개발하고 토양수분을 예측하는 단계로 입력자료로 위성영상 자료와 관측자료를 융합하여 사용하였다. 위성영상 자료로는 보정된 MODIS LST와 MODIS NDV를 구축하였고 일단위 강수량 및 일조시간의 기상자료는 기상청으로부터 전국 71개 지점에 대해 구축하여 IDW 공간보간기법을 이용한 공간자료로 구축하였다. 토양수분 결과를 비교하기 위한 관측 토양수분은 자동농업기상관측(Automated Agriculture Observing System, AAOS)지점에서 2013년 1월부터 2015년 12월까지의 실측 일단위 토양수분 자료를 구축하여 사용하였다. 다중분위회귀분석 모형은 LST 인자를 중심으로 각각의 분위(0.05, 0.25, 0.5, 0.75, 0.95)에 해당되는 값의 회귀식을 NDVI, 강수 입력자료를 독립인자로서 조합하여 계절 및 토성에 따른 총 80개의 회귀식을 산정하였다. 관측 토양수분과 모의 토양수분을 비교한 결과 $R^2$가 0.70 (철원), 0.90 (춘천), 0.85 (수원), 0.65 (서산), 0.78 (청주), 0.82 (전주), 0.62 (순천), 0.63 (진주), 0.78 (보성)로 높은 상관성을 보였다. 본 연구에서는 다중분위회귀 모형의 성능을 검증하기 위해 기존의 다중선형회귀모형의 결과와 비교하여 크게 개선됨을 나타냈다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7134-7147
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• 2014

This study examined one of the contemporary issues that may be interesting to academics and practitioners regarding the driving force of the growth rate for the firms belonging to the chaebols in the Korean capital markets. With respect to the empirical results obtained from two hypothesis tests, the first hypothesis was to identify any financial determinants on the growth rate by applying both dynamic panel data and static panel data models. The debt ratios relevant to the book- and market-value showed their positive relationships with the DV of GROWTH1, along with other significant IDVs such as one-period lagged DV of GROWTH_1, SIZE1 and FOS with statistical significance. Second, by employing conditional quantile regression (CQR) analysis, the control variables, such as ROA, SMARKET, time dummy variable of F2010 and F2011, and the industry dummies of IND3 and IND10, provided evidence of their significant influences on DV of GROWTH1.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.174-182
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• 2018

Given the ongoing debate in many aspects of finance, more attention may need to focus on corporate R&D expenditures. This study empirically tests financial determinants of R&D expenditures for NYSE-listed and KOSPI-listed firms. Three major hypotheses were postulated to test for corporate R&D outlay. First, proposed variables such as one-year lagged R&D expenditures, market value based leverage, profitability and cash holdings showed significant influence on corporate R&D costs for the sample firms. Moreover, financial factors inclusive of squared one-year lagged R&D expenditures, the interaction effect between one-lagged R&D expenditures and high-growth firm, non-debt tax shield, Tobin's q and a dummy variable to explain differences in accounting treatment between the U.S. and Korea, revealed significant differences between the two samples. Finally, in the conditional quantile regression (CQR) analysis for the R&D-related variables in relation to corporate growth rate, it was found that the NYSE-listed firms had a statistically significant linkage between growth potential and one-year lagged R&D expenditures at lower quantile levels. This study may shed new light on identifying financial factors affecting differences between the U.S. market (as an advanced market) and the Korean market (as an emerging market) regarding the optimal level of R&D investments for shareholders.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.423-436
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• 2011

Many of the theoretical studies have considered herd behavior as a source of the volatility in financial markets, but there have been few empirical studies on the dynamic herding due to the technical difficulty of detecting herd behavior with time-series data. In this context, this paper proposes a new method for measuring time-varying herd behavior based on QR-GARCH model. Using daily data of KOSPI stocks, this paper provides some empirical evidence for strong and volatile herding among traders of stocks of medium firms, and shows that time-varying herd behavior in traders of some stocks has persistent autocorrelation.