We focus on high-breakdown methods, which can deal with a substantial fraction of outliers in the data. We give an overview of recent high-breakdown robust methods for multivariate settings such as ...

An introduction to the application of modern multivariate methods used in the social sciences, with particular focus on latent variable models for continuous observed variables, and their application ...

Frank Henry, Multivariate Analysis and Ordinal Data, American Sociological Review, Vol. 47, No. 2 (Apr., 1982), pp. 299-304 ...

An introduction to the application of modern multivariate methods used in the social sciences, with particular focus on latent variable models (including factor analysis, structural equation models, ...

Multivariate analysis is commonly used when we have more than one outcome variables for each observation. For instance, a survey of American adults’ physical and mental health might measure each ...

Even a change in the correlation of those parameters may indicate a problem. For that reason, multivariate monitoring, or multivariate statistics, is applied to these parameters. Multivariate analysis ...

Multivariate analysis uses statistical tools such as multiple regression analysis, cluster analysis and conjoint analysis to determine the relationships between factors.

Through logistic regression analysis, it was found that pain severity, walking ability, self-care ability, physical function, ...