Variable selection in the kernel Cox regression

심주용 한국데이터정보과학회 2011 한국데이터정보과학회지 Vol.22 No.4

In machine learning and statistics it is often the case that some variables are not important, while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables in the kernel Cox regression. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of relevant variables in the kernel Cox regression. Experimental results are then presented which indicate the performance of the proposed method.

선형 응답률 모형에서 초모집단 모형의 비모수적 함수 추정을 이용한 무응답 편향 보정 추정

심주용,신기일 한국통계학회 2021 응용통계연구 Vol.34 No.6

A large number of non-responses are occurring in the sample survey, and various methods have been developed to deal with them appropriately. In particular, the bias caused by non-ignorable non-response greatly reduces the accuracy of estimation and makes non-response processing difficult. Recently, Chung and Shin (2017, 2020) proposed an estimator that improves the accuracy of estimation using parametric super-population model and response rate model. In this study, we suggested a bias corrected non-response mean estimator using a non-parametric function generalizing the form of a parametric super-population model. We confirmed the superiority of the proposed estimator through simulation studies. 표본조사에서는 다수의 무응답이 발생하며 이를 적절히 처리하는 다양한 방법이 개발되었다. 특히 무응답이 관심변수에 영향을 받고 이로 인해 발생한 편향은 추정의 정확성을 크게 떨어뜨리며 무응답 처리를 어렵게 한다. 최근 Chung과 Shin (2017, 2020)은 알려진 모수적 초모집단 모형과 응답률 모형을 이용하여 추정의 정확성을 향상한 추정량을 제안하였다. 본 연구에서는 초모집단 모형의 형태를 일반화하여 비모수적 함수 형태를 설정한 후 이를 기반으로 얻어진 편향을 적절히 처리한 편향 보정 평균추정량을 제안하였다. 모의실험을 통해 본 연구에서 제안한 방법의 우수성을 확인하였다.

Forecasting LNG prices with the kernel vector autoregressive model

심주용,조홍종 한국자원공학회 2020 Geosystem engineering Vol.23 No.1

LNG prices in the Northeast Asian countries are closely related multivariate time series, because they are traded with similar contracts. For the analysis of multivariate time series data, the vector autoregressive model is one of the most successful tools to use. But the vector autoregressive model assumes a linear relationship between the present and previous data, which sometimes provides unreliable results. To address this problem, we applied the weighted version of the least squares support vector machine to the vector autoregressive model. In numerical studies with liquefied natural gas importing prices in four Asian countries, comparisons with other methods indicated that the proposed kernel vector autoregressive model provides more satisfying results on fitting and forecasting for multivariate time series.

비선형 평균 일반화 이분산 자기회귀모형의 추정

심주용,이장택,Shim, Joo-Yong,Lee, Jang-Taek 한국데이터정보과학회 2010 한국데이터정보과학회지 Vol.21 No.5

최소제곱 서포트벡터기계는 비선형회귀분석과 분류에 널리 쓰이는 커널기법이다. 본 논문에서는 금융시계열자료의 평균 및 변동성을 추정하기 위하여 평균의 추정 방법으로는 가중최소제곱 서포트벡터기계, 변동성의 추정 방법으로는 최소제곱 서포트벡터기계를 사용하는 비선형 평균 일반화 이분산 자기회귀모형을 제안한다. 제안된 모형은 선형 일반화 이분산 자기회귀모형 및 선형 평균 일반화 이분산 자기회귀모형보다 더 나은 추정 능력을 가진다는 것을 실제자료의 추정을 통하여 보였다. Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.

Forecasting volatility via conditional autoregressive value at risk model based on support vector quantile regression

심주용,황창하 한국데이터정보과학회 2011 한국데이터정보과학회지 Vol.22 No.3

The conditional autoregressive value at risk (CAViaR) model is useful for risk management, which does not require the assumption that the conditional distribution does not vary over time but the volatility does. But it does not provide volatility forecasts, which are needed for several important applications such as option pricing and portfolio management. For a variety of probability distributions, it is known that there is a constant relationship between the standard deviation and the distance between symmetric quantiles in the tails of the distribution. This inspires us to use a support vector quantile regression (SVQR) for volatility forecasts with the distance between CAViaR forecasts of symmetric quantiles. Simulated example and real example are provided to indicate the usefulness of proposed forecasting method for volatility.

SVQR with asymmetric quadratic loss function

심주용,김말숙,석경하 한국데이터정보과학회 2015 한국데이터정보과학회지 Vol.26 No.6

Support vector quantile regression (SVQR) can be obtained by applying support vector machine with a check function instead of an e-insensitive loss function into the quantile regression, which still requires to solve a quadratic program (QP) problem which is time and memory expensive. In this paper we propose an SVQR whose objective function is composed of an asymmetric quadratic loss function. The proposed method overcomes the weak point of the SVQR with the check function. We use the iterative procedure to solve the objective problem. Furthermore, we introduce the generalized cross validation function to select the hyper-parameters which affect the performance of SVQR. Experimental results are then presented, which illustrate the performance of proposed SVQR.

Expected shortfall estimation using kernel machines

심주용,황창하 한국데이터정보과학회 2013 한국데이터정보과학회지 Vol.24 No.3

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR),restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and sup-port vector expectile regression (SVER). These kernel machines have obvious advan-tages such that they achieve nonlinear model but they do not require the explicit form of nonlinear mapping function. Moreover they need no assumption about the underly-ing probability distribution of errors. Through numerical studies on two artificial and two real data sets we show their effectiveness on the estimation performance at various confidence levels.

Estimating multiplicative competitive interaction model using kernel machine technique

심주용,김말숙,박혜정 한국데이터정보과학회 2012 한국데이터정보과학회지 Vol.23 No.4

We propose a novel way of forecasting the market shares of several brands simultaneously in a multiplicative competitive interaction model, which uses kernel regression technique incorporated with kernel machine technique applied in support vector machines and other machine learning techniques. Traditionally, the estimations of the market share attraction model are performed via a maximum likelihood estimation procedure under the assumption that the data are drawn from a normal distribution. The proposed method is shown to be a good candidate for forecasting method of the market share attraction model when normal distribution is not assumed. We apply the proposed method to forecast the market shares of 4 Korean car brands simultaneously and represent better performances than maximum likelihood estimation procedure.

Variance function estimation with LS-SVM for replicated data

심주용,박혜정,석경하 한국데이터정보과학회 2009 한국데이터정보과학회지 Vol.20 No.5

In this paper we propose a variance function estimation method for replicated data based on averages of squared residuals obtained from estimated mean function by the least squares support vector machine. Newton-Raphson method is used to obtain associated parameter vector for the variance function estimation. Furthermore, the cross validation functions are introduced to select the hyper-parameters which affect the performance of the proposed estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

2P-661 겨울철 설해예방을 위한 근적외선 흡수 발열필름 연구

심주용,유국현,김정엽,김기혁 한국공업화학회 2017 한국공업화학회 연구논문 초록집 Vol.2017 No.0