• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.787-797
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• 2002

Partial residual plots, augmented partial residual plots and CERES plots are basic diagnostic tools for dealing with curvature as a function of specific predictors in regression problem. However, it is known that these plots can miss a curve or show a false curve in some cases such as predictors are related each other. Dynamic display of these plots is developed and applied. Examples demonstrate that dynamic plots are useful for obtaining additional Information on the curvature.

• Journal of the Korean Data and Information Science Society
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• 제11권1호
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• pp.73-81
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• 2000

The variance inflation factor can be expressed by the square of the ratio of t-statistics associated with slopes of partial regression and partial residual plots. Disagreement of two sides in the interpretation can be occurred, and we analyze it with some illustrations.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.311-317
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• 2001

It is necessary to check the linearity of selected covariates in regression diagnostics. There are various graphical methods using residual plots such as partial residual plots, augmented partial residual plots and combining conditional expectation and residual plots. In this paper, we propose the modified pseudolikelihood ratio test statistics based on these residual plots to test linearity of selected covariate. These test statistics which measure the distance between the nonparametric and parametric models are derived as a ratio of quadratic forms. The approximate distribution of these statistics is calculated numerically by using three moments. The power comparison of these statistics is given.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.529-537
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• 2004

This article concerns dynamic graphical methods for visualizing a curvature in regression problem in which some predictors enter nonlinearly. A sequence of augmented partial residual plot or partial residual plot updated by the change of linear combination of two predictors are constructed. Examples demonstrate that the suggested methods can be used to reduce the dimension of explanatory variables as well as to capture a curvature.

• Communications for Statistical Applications and Methods
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• 제16권3호
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• pp.495-500
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• 2009

Two diagnostic plots, added variable plot and partial residual plot, are proposed when a new explanatory variable is linearly added to constrained regressions. They are useful for investigating the effect of adding an explanatory variable to the constrained regression. They visually give an overall impression of the strength of linear relationship between response variable and added variable. A numerical example is provided for illustration.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.521-531
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• 2002

Various diagnostic techniques for identifying influential observations are mostly based on the deletion of a single observation. While such techniques can satisfactorily identify influential observations in many cases, they will not always be successful because of some mask effect. It is necessary, therefore, to develop techniques that examine the potentially influential effects of a subset of observations. The partial regression plots can be used to examine an influential observation for a single parameter in multiple linear regression. However, it is often desirable to detect influential observations for a subset of regression parameters when interest centers on a selected subset of independent variables. Thus, we propose a diagnostic measure which deals with detecting influential observations on a subset of regression parameters. In this paper, we propose a measure M, which can be effectively used for the detection of influential observations on a subset of regression parameters in multiple linear regression. An illustrated example is given to show how we can use the new measure M to identify influential observations on a subset of regression parameters.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.1-12
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• 2000

We explore the structure and usefulness of CERES plot as a basic tool for dealing with curvature as a function of the new predictor in nonlinear regression. If a predictor has a nonlinear effect and there are nonlinear relationships among the predictors the partial residual plot and augmented partial residual plot are not able to display the correct functional form of the predictor. Unlike these plots the CERES plot can show the correct from. In situations where nonlinearity exists in two predictors we extend the idea of CERES plot to three dimensions, This is illustrated by simulated data.

• 응용통계연구
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• 제34권4호
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• pp.589-597
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• 2021

단순회귀와 다중회귀에서 회귀계수의 의미는 차이가 있고 회귀계수의 추정값은 같지 않을 뿐 아니라 그 부호가 서로 다른 경우도 발생한다. 회귀모형에서 설명변수의 상대적 기여도의 파악은 회귀분석의 수행의 중요한 부분이다. 표준화 회귀모형에서 표준화 회귀계수는 해당 설명변수를 제외한 나머지 설명변수의 값이 고정되어있는 상황에서 설명변수가 표준편차만큼 증가하였을 때 반응변수가 표준편차를 기준으로 얼마나 변화했는가로 해석할 수 있지만 표준화 회귀계수의 크기가 각 설명변수의 상대적 중요도를 나타내는 척도라고 할 수 없음은 잘 알려져 있다. 본 논문에서는 다중회귀에서 회귀계수의 추정량을 상관계수와 결정계수의 함수로 나타내고 이를 추가적인 설명력과 추가적인 결정계수의 관점에서 생각해 본다. 또한 다양한 산점도에서의 상관계수와 회귀계수 추정값의 관계를 알아보고 설명변수가 두 개인 경우에 구체적으로 적용해 본다.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2008년도 한국우주과학회보 제17권2호
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• pp.24.2-24.2
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• 2008

We have examined the relative contributions of representative space weather proxies (geomagnetic aa index) to global warming (Global temperature anomaly) and compared them with that of green house effect characterized CO2 content from 1868 to 2005. For this we used Hadcrut3 temperature anomaly (Ta) data, aa index taken at two anti-podal subauroral stations (Canberra Australia and hartland England), and the CO2 data come from historical ice core records. From the comparison between Ta and aa index, we found several interesting results: (1) the linear correlation coefficient between two parameters increases until 1990 and then decreases rapidly, and (2) the scattered plots between two parameters shows different patterns before and after 1990. A partial correlation of Ta and two quantities (aa, CO2) also shows that the geomagnetic effect (aa index) is dominant until about 1990 and the CO2 effect becomes much more important after then. These results imply that the green house effect become very important since at least 1990. For a further analysis, we simply assume that Ta (total) = Ta (aa) + Ta (CO2) and made a linear regression between Ta and aa index from 1868 to 1990. A linear model is then made from the linear regression between energy consumption (a proxy of CO2 effect) and Ta (total) - Ta (aa) since 1990. This linear model makes it possible to predict the temperature anomaly in 2030, about 1 degree higher than the present temperature, which is much larger than in the previous century.

• KSBB Journal
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• 제21권1호
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• pp.59-67
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• 2006

본 연구에서는 2차원 형광스펙트럼의 PCA 분석을 통하여 발효 공정을 모니터링하고 PCR과 PLS과 같은 다변량 분석기법을 이용하여 공정을 모델링하였다. 재조합 대장균 E. coli 와 효모 S.cerevisiae의 발효 공정 중에 얻어진 많은 양의 2차원 형광스펙트럼 자료는 우선 PCA를 통해 축소된다. 그리고 PCA에서 주성분점수와 적재 산점도는 발효 공정의 정성적 경향을 묘사하기 위해 사용되었다. 또한, PCR과 PLS는 2차원 형광스펙트럼의 분석을 위해 사용되었으며 PLS모델이 보정과 예측 능력에서 PCR모델보다 조금 더 우수한 성능을 나타냈다. 따라서 2차원 형광스펙트럼 자료를 이용하여 생물공정을 모델링 하고자 할 때는 PCR 방법보다는 PLS 방법을 사용하는 것이 유리할 것이다.