• Title/Summary/Keyword: Linear Multivariate Regression

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Test for an Outlier in Multivariate Regression with Linear Constraints

  • Kim, Myung-Geun
    • Communications for Statistical Applications and Methods
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    • v.9 no.2
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    • pp.473-478
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    • 2002
  • A test for a single outlier in multivariate regression with linear constraints on regression coefficients using a mean shift model is derived. It is shown that influential observations based on case-deletions in testing linear hypotheses are determined by two types of outliers that are mean shift outliers with or without linear constraints, An illustrative example is given.

Biplots of Multivariate Data Guided by Linear and/or Logistic Regression

  • Huh, Myung-Hoe;Lee, Yonggoo
    • Communications for Statistical Applications and Methods
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    • v.20 no.2
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    • pp.129-136
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    • 2013
  • Linear regression is the most basic statistical model for exploring the relationship between a numerical response variable and several explanatory variables. Logistic regression secures the role of linear regression for the dichotomous response variable. In this paper, we propose a biplot-type display of the multivariate data guided by the linear regression and/or the logistic regression. The figures show the directional flow of the response variable as well as the interrelationship of explanatory variables.

Alternative Derivation of Stepwise Multivariate Linear Regression (段階的 多變量 線型回歸에 관하여)

  • 申敏雄;金周成
    • 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.

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INFLUENCE ANALYSIS FOR A LINEAR HYPOTHESIS IN MULTIVARIATE REGRESSION MODEL

  • Kim, Myung-Geun
    • Journal of applied mathematics & informatics
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    • v.13 no.1_2
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    • pp.479-485
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    • 2003
  • The influence of observations on the Wilks' lambda test of a linear hypothesis in multivariate regression is investigated using the local influence method. The perturbation scheme of case-weights is considered. A numerical example is given to show the effectiveness of the local influence method in identifying the influential observations.

Multivariate statistical analysis of the comparative antioxidant activity of the total phenolics and tannins in the water and ethanol extracts of dried goji berry (Lycium chinense) fruits

  • Kim, Joo-Shin;Kimm, Haklin Alex
    • 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.

More on directional regression

  • Kim, Kyongwon;Yoo, Jae Keun
    • Communications for Statistical Applications and Methods
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    • v.28 no.5
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    • pp.553-562
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    • 2021
  • Directional regression (DR; Li and Wang, 2007) is well-known as an exhaustive sufficient dimension reduction method, and performs well in complex regression models to have linear and nonlinear trends. However, the extension of DR is not well-done upto date, so we will extend DR to accommodate multivariate regression and large p-small n regression. We propose three versions of DR for multivariate regression and discuss how DR is applicable for the latter regression case. Numerical studies confirm that DR is robust to the number of clusters and the choice of hierarchical-clustering or pooled DR.

Bayesian Estimation for the Multiple Regression with Censored Data : Mutivariate Normal Error Terms

  • Yoon, Yong-Hwa
    • 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.

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Robust Estimation and Outlier Detection

  • Myung Geun Kim
    • Communications for Statistical Applications and Methods
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    • v.1 no.1
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    • pp.33-40
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    • 1994
  • The conditional expectation of a random variable in a multivariate normal random vector is a multiple linear regression on its predecessors. Using this fact, the least median of squares estimation method developed in a multiple linear regression is adapted to a multivariate data to identify influential observations. The resulting method clearly detect outliers and it avoids the masking effect.

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Multivariate Statistical Analysis and Prediction for the Flash Points of Binary Systems Using Physical Properties of Pure Substances (순수 성분의 물성 자료를 이용한 2성분계 혼합물의 인화점에 대한 다변량 통계 분석 및 예측)

  • Lee, Bom-Sock;Kim, Sung-Young
    • 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.

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Residuals Plots for Repeated Measures Data

  • PARK TAESUNG
    • Proceedings of the Korean Statistical Society Conference
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    • 2000.11a
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    • pp.187-191
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    • 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.

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