As a generalization of the canonical correlation analysis to k random vectors, the common canonical variates model was recently proposed based on the assumption that the canonical variates have the same coefficients in all k sets of variables, and is applicable to many cases. In this article, we apply the local influence method in this model to study the impact of minor perturbations of data. The method is non-standard because of the restrictions imposed on the coefficients. Besides investigating the joint local influence of the observations, we also obtain the elliptical norm of the empirical influence function as a special case of local influence diagnostics. Based on the proposed diagnostics, we find that the results of common canonical ...
AbstractLocal influence is a method of sensitivity analysis for assessing the influence of small per...
Statistical analyses are usually based on models. However, a model is almost always only an approx- ...
In restricted statistical models, since the first derivatives of the likelihood displacement are oft...
The first order local influence approach is adopted in this paper to assess the local influence of o...
The local influence method is adapted to canonical correlation analysis for the purpose of investiga...
AbstractIn principal components analysis, the influence function and local influence approaches have...
In principal components analysis, the influence function and local influence approaches have been we...
The influence of observations on the outcome of an analysis is of importance in statistical data ana...
It is important to understand the influence of data and model assumptions on the results of a statis...
AbstractThe multivariate probit model is very useful for analyzing correlated multivariate dichotomo...
Biometrics 2001 Dec;57(4):1166-72 Related Articles, Books, LinkOut Local influence to detect influen...
Biometrics 2001 Dec;57(4):1166-72 Related Articles, Books, LinkOut Local influence to detect influen...
Biometrics 2001 Dec;57(4):1166-72 Related Articles, Books, LinkOut Local influence to detect influen...
Suggested diagnostics for influence on the estimated regression coefficients in a general-ized linea...
We study the Local Influence on the General Linear Model with a perturbation scheme in the variance-...
AbstractLocal influence is a method of sensitivity analysis for assessing the influence of small per...
Statistical analyses are usually based on models. However, a model is almost always only an approx- ...
In restricted statistical models, since the first derivatives of the likelihood displacement are oft...
The first order local influence approach is adopted in this paper to assess the local influence of o...
The local influence method is adapted to canonical correlation analysis for the purpose of investiga...
AbstractIn principal components analysis, the influence function and local influence approaches have...
In principal components analysis, the influence function and local influence approaches have been we...
The influence of observations on the outcome of an analysis is of importance in statistical data ana...
It is important to understand the influence of data and model assumptions on the results of a statis...
AbstractThe multivariate probit model is very useful for analyzing correlated multivariate dichotomo...
Biometrics 2001 Dec;57(4):1166-72 Related Articles, Books, LinkOut Local influence to detect influen...
Biometrics 2001 Dec;57(4):1166-72 Related Articles, Books, LinkOut Local influence to detect influen...
Biometrics 2001 Dec;57(4):1166-72 Related Articles, Books, LinkOut Local influence to detect influen...
Suggested diagnostics for influence on the estimated regression coefficients in a general-ized linea...
We study the Local Influence on the General Linear Model with a perturbation scheme in the variance-...
AbstractLocal influence is a method of sensitivity analysis for assessing the influence of small per...
Statistical analyses are usually based on models. However, a model is almost always only an approx- ...
In restricted statistical models, since the first derivatives of the likelihood displacement are oft...