• Journal of the Korean Statistical Society
• /
• 제21권1호
• /
• pp.35-46
• /
• 1992

On Box-Cox transformation, one or few responses are influential on transformation parameter estimator. To detect influential observatins, several diagnostics (Cook and Wang 1983, Hinkley and Wang 1988, Lawrance 1988, Tsai and Wu 1990) have been suggested. We compare these diagnostics and denote the necessity of multiple cases deletion which is important especially when the masking effect is present. Also, analytic expression of Tsai and Wu's diagnostic is given. We suggest a computationally feasible and useful algorithm based on the basic building blocks, and present descriptive examples using artificial data.

• Communications for Statistical Applications and Methods
• /
• 제12권3호
• /
• pp.635-642
• /
• 2005

In statistical diagnostics a large number of influence measures have been proposed for identifying outliers and influential observations. However it seems to be few accounts of the influence diagnostics on test statistics. We study influence analysis on the likelihood ratio test statistic whether the two sets of variables are uncorrelated with one another or not. The influence of observations is measured using the case-deletion approach, the influence function. We compared the proposed influence measures through two illustrative examples.

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2000년도 추계학술발표회 논문집
• /
• pp.187-191
• /
• 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.

• 응용통계연구
• /
• 제25권2호
• /
• pp.321-330
• /
• 2012

In this paper we study some basic statistics in quantile regression. In particular, we investigate the residual, goodness-of-fit statistic and the effect of one or few observations on estimates of regression coefficients. In addition, we compare the proposed goodness-of-fit statistic with the statistic considered by Koenker and Machado (1999). An illustrative example based on real data sets is given to see the numerical performance of the proposed basic statistics.

• Communications for Statistical Applications and Methods
• /
• 제10권1호
• /
• pp.125-134
• /
• 2003

Standard estimation methods for linear mixed models are sensitive to influential observations. However, tools and concepts for linear mixed model diagnostics are rudimentary until now and research is heavily demanded in linear mixed models. In this paper, we consider two diagnostics to evaluate the effects of individual observations in the estimation of fixed effects for linear mixed models. Those are Cook's distance and COVRATIO. Results of our limited simulation study suggest that the Cook's distance is not good statistical quantity in linear mixed models. Also calibration point for COVRATIO seems to be quite conservative.

• Journal of applied mathematics & informatics
• /
• 제25권1_2호
• /
• pp.489-493
• /
• 2007

Two diagnostic measures based on the likelihood distance for constrained regression with linear constraints on regression coefficients are derived. They are used for identifying influential observations in constrained regression. A numerical example is provided for illustration.

• Communications for Statistical Applications and Methods
• /
• 제15권2호
• /
• pp.229-234
• /
• 2008

A Cook-type distance is considered for investigating the influence of observations in constrained regression models. Its exact sampling distribution is derived, which is used for judging whether each observation is influential or not. A numerical example is provided for illustration.

• Communications for Statistical Applications and Methods
• /
• 제14권2호
• /
• pp.281-286
• /
• 2007

Cook's distance is generalized to the multiple linear regression with linear constraints on regression coefficients. It is used for identifying influential observations in constrained regression models. A numerical example is provided for illustration.

• Communications for Statistical Applications and Methods
• /
• 제1권1호
• /
• pp.33-40
• /
• 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.

• 품질경영학회지
• /
• 제21권1호
• /
• pp.55-64
• /
• 1993

This article is concerned with procedures for detecting one or more outliers or influential observations in a linear regression model. A test procedure, based on recursive residuals is proposed and developed The power of the test procedure to identify one or more outliers is investigated through simulation, and its relevance to the number and configuration of the outlier.