• Journal of the Korean Chemical Society
• /
• v.44 no.2
• /
• pp.102-108
• /
• 2000

Relative retention times (RRTs) of RAH molecules in HPLC are trained and predicted intesting sets using a multiple linear regression (NLR) and an artificial neural network (ANN). The maindescriptors in QSRR are molecular connectivity ($^1X_v,\;^2X_v$), the length-to-breadth ratios (L/B), and molecular dipole moment(D). L/B which is related with slot model is a good descripter in ANN, but isn't in MLR. Varainces which show the accuracy of prediction times in testing sets are 0.0099, 0.0114 for ANN and MLR, respectively. It was shown that ANN can exceed the MLR in prediction accuracy.

• The Korean Journal of Applied Statistics
• /
• v.21 no.1
• /
• pp.19-33
• /
• 2008

Linear regression method, proposed by Haseman and Elston(1972), for detecting linkage to a quantitative trait of sib pairs is a linkage testing method for a single locus and a single trait. However, multivariate methods for detecting linkage are needed, when information from each of several traits that are affected by the same major gene are available on each individual. Amos et al. (1990) extended the regression method of Haseman and Elston(1972) to incorporate observations of two or more traits by estimating the principal component linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. But, it is impossible to control the probability of type I errors with this method at present, since the exact distribution of the statistic that they use is yet unknown. In this paper, we propose a multivariate nonparametric trend test for detecting linkage to multiple traits. We compared with a simulation study the efficiencies of multivariate nonparametric trend test with those of the method developed by Amos et al. (1990) for quantitative traits data. For multivariate nonparametric trend test, the results of the simulation study reveal that the Type I error rates are close to the predetermined significance levels, and have in general high powers.

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

• Clinics in Shoulder and Elbow
• /
• v.16 no.1
• /
• pp.63-72
• /
• 2013

In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, $R^2$ and the influence of independent variables on result as a regression coefficient, ${\beta}$. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient${\beta}$, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

• The Korean Journal of Applied Statistics
• /
• v.34 no.4
• /
• pp.659-669
• /
• 2021

Multivariate regression often appears in longitudinal or functional data analysis. Since multivariate regression involves multi-dimensional response variables, it is more strongly affected by the so-called curse of dimension that univariate regression. To overcome this issue, Yoo (2018) and Yoo (2019a) proposed three model-based response dimension reduction methodologies. According to various numerical studies in Yoo (2019a), the default method suggested in Yoo (2019a) is least sensitive to the simulated models, but it is not the best one. To release this issue, the paper proposes an selection algorithm by comparing the other two methods with the default one. This approach is called principal selected response reduction. Various simulation studies show that the proposed method provides more accurate estimation results than the default one by Yoo (2019a), and it confirms practical and empirical usefulness of the propose method over the default one by Yoo (2019a).

• Communications for Statistical Applications and Methods
• /
• v.18 no.1
• /
• pp.13-21
• /
• 2011

This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

• Journal of the Korean Chemical Society
• /
• v.45 no.5
• /
• pp.422-428
• /
• 2001

Retention relative times(RRTs) of PAH molecules and their derivatives in gas chromatography are trained and predicted in testing sets using a multiple linear regression(MLR) and an artificial neural network(ANN). The main descriptors of PAHs and their derivatives in QSRR are the square root of molecular weight(sqmw), molecular connectivity($^1{\chi}_v$), molecular dipole moment(D) and length-to-breadth ratios(L/B). The results of MLR shows that a heavy molecule has a propensity for long retention time. L/B closely related with slot model is a good descriptor in MLR. On the other hand, ANN which is not effected by the linear dependencies among the descriptors were exclusively based on molecular weight and molecular dipole moment. The variances which shows the accuracy of prediction for retention times in testing sets are 1.860, 0.206 for MLR and ANN, respectively. It was shown that ANN can exceed the MLR in prediction accuracy.

• Journal of Korea Water Resources Association
• /
• v.55 no.3
• /
• pp.229-243
• /
• 2022

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2006.05a
• /
• pp.775-779
• /
• 2006

강우자료의 구축은 수문해석에 있어 가장 기본적이며 중요한 단계라 할 수 있다. 하지만 수문 관측 자료의 경우 결측치가 존재하여 그에 대한 보정이 필요한 경우가 종종 발생하게 된다. 따라서 수문자료의 분석을 수행하기에 앞서 우선 자료에 대한 검정을 실시하고, 결측치가 존재할 경우는 이를 보정하여 분석을 수행하여야 한다. 본 연구에서는 다변량통계기법의 하나인 다중회귀분석을 이용하여 강우 결측치를 보정하였다. 본 연구에서는 다중공선성과 자기상관에 대하여 고려한 다중회귀모형을 구성하였다. 모형의 구성시 모든 결측지점에 적용이 가능하지 않아 일반성이 떨어짐을 확인 할 수 있었지만, 모형이 구성될 경우 통계적 적합도와 유의수준을 확인 할 수 있는 장점이 있었으며, 다중회귀모형이 구성되는 경우 좋은 보정 결과를 주는 것을 확인 할 수 있었다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.5B
• /
• pp.445-458
• /
• 2010

The effects of geographical and climatic factors on annual and monthly pan and Penman evaporation were analyzed. 52 climatological stations were selected and trend analyses were performed. Furthermore, cluster analysis and multiple linear regression analysis were performed to understand the effects of geographical and climatic factors on pan and Penman evaporation. Based on stepwise multiple linear regression analysis, annual pan evaporation is proved to be mainly controlled by urbanization as geographical factor, and annual pan evaporation is also controlled by temperature, relative humidity, wind speed, and solar radiation as climatic factor. Especially wind speed is considered to be most significant climatic factor which affects pan evaporation. Meanwhile, Penman evaporation is not affected by geographical factors but it is affected by climate factors such as temperature, relative humidity, wind speed, and solar radiation except precipitation. Furthermore, the study results show that only proximity to coast affects pan evaporation trend on July; however, geographical and climatic factors do not affect pan evaporation trends in annual basis and monthly basis (January, April, and October). On the other hand, Penman evaporation trends were not affected by geographical factors in annual and monthly basises.