• Korean Journal of Food Science and Technology
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• v.51 no.3
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• pp.227-236
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• 2019

Antioxidant activity in water and ethanol extracts of dried Lycium chinense fruit, as a result of the total phenolic and tannin content, was measured using a number of chemical and biochemical assays for radical scavenging and inhibition of lipid peroxidation, with the analysis being extended by applying a bootstrapping statistical method. Previous statistical analyses mostly provided linear correlation and regression analyses between antioxidant activity and increasing concentrations of phenolics and tannins in a concentration-dependent mode. The present study showed that multiple component or multivariate analysis by applying multiple regression analysis or regression planes proved more informative than linear regression analysis of the relationship between the concentration of individual components and antioxidant activity. In this paper, we represented the multivariate analysis of antioxidant activities of both phenolic and tannin contents combined in the water and ethanol extracts, which revealed the hidden observations that were not evident from linear statistical analysis.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.243-248
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• 1995

The problem of optimal design for a nonparametric regression with binary data is considered. The aim of the statistical analysis is the estimation of a quantal response surface in two dimensions. Bias, variance and IMSE of kernel estimates are derived. The optimal design density with respect to asymptotic IMSE is constructed.

• Journal of the Korean Statistical Society
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• v.7 no.2
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• pp.105-108
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• 1978

Freund, Vail, and Ross, Goldberger and Jochems and Goldberger have given some results for the stepwise estimation of the parameters of a univariate regression model, D.G. Kabe gave similar results for a multivariate linear regression model. We give here alternative derivation of some results derived by D.G. Kabe.

• Journal of the Korean Statistical Society
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• v.9 no.2
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• pp.173-180
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• 1980

Hocking, Rao, Rosenberg and Levy and Walls and Weeks have given some results for the misspecification in univariate regression model. We give similar results for a multivariate regression model.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.165-172
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• 1998

This paper considers a linear regression model with censored data where each error term follows a multivariate normal distribution. In this paper we consider the diffuse prior distribution for parameters of the linear regression model. With censored data we derive the full conditional densities for parameters of a multiple regression model in order to obtain the marginal posterior densities of the relevant parameters through the Gibbs Sampler, which was proposed by Geman and Geman(1984) and utilized by Gelfand and Smith(1990) with statistical viewpoint.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.1-13
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• 2018

In many studies, a researcher attempts to describe a population where units are measured for multiple outcomes, or responses. In this paper, we present an efficient procedure based on ranked set sampling to estimate and perform hypothesis testing on a multivariate mean. The method is based on ranking on an auxiliary covariate, which is assumed to be correlated with the multivariate response, in order to improve the efficiency of the estimation. We showed that the proposed estimators developed under this sampling scheme are unbiased, have smaller variance in the multivariate sense, and are asymptotically Gaussian. We also demonstrated that the efficiency of multivariate regression estimator can be improved by using Ranked set sampling. A bootstrap routine is developed in the statistical software R to perform inference when the sample size is small. We use a simulation study to investigate the performance of the method under known conditions and apply the method to the biomarker data collected in China Health and Nutrition Survey (CHNS 2009) data.

• Journal of the Korean Statistical Society
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• v.35 no.1
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• pp.105-114
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• 2006

In this paper we propose a simple and computationally attractive difference-based variance estimator in nonparametric regression models with multivariate predictors. We show that the estimator achieves $n^{-1/2}$ rate of convergence for regression functions with only a first derivative when d, the dimension of the predictor, is less than or equal to 4. When d > 4, the rate turns out to be $n^{-4/(d+4)}$ under the first derivative condition for the regression functions. A numerical study suggests that the proposed estimator has a good finite sample performance.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.107-115
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• 2012

Yoo and Cook (2007) developed an optimal sufficient dimension reduction methodology for the conditional mean in multivariate regression and it is known that their method is asymptotically optimal and its test statistic has a chi-squared distribution asymptotically under the null hypothesis. To check the effect of dimension used in estimation on regression coefficients and the explanatory power of the conditional mean model in multivariate regression, we applied their method to several simulated data sets with various dimensions. A small simulation study showed that it is quite helpful to search for an appropriate dimension for a given data set if we use the asymptotic test for the dimension as well as results from the estimation with several dimensions simultaneously.

• Journal of the Korean Institute of Gas
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• v.11 no.3
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• pp.13-18
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• 2007

The multivariate statistical analysis, using the multiple linear regression(MLR), have been applied to analyze and predict the flash points of binary systems. Prediction for the flash points of flammable substances is important for the examination of the fire and explosion hazards in the chemical process design. In this paper, the flash points are predicted by MLR based on the physical properties of pure substances and the experimental flash points data. The results of regression and prediction by MLR are compared with the values calculated by Raoult's law and Van Laar equation.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.21-32
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• 2002

We searched out in the votes data of the State of Florida at presidential election 2000. We used a multivariate regression analysis. We got there were several outliers including Palm Beach County. It means that we should analyze the number of disqualified ballots which were double-punched as well as the votes, to insist the " Butterfly Ballot" made Palm Beach outlier.