Multivariate analyses such as principal component analysis were among the first statistical methods employed to extract information from genetic markers. From their early applications to current ...

Agglomerative-polythetic methods (commonly known as `similarity methods') of hierarchically classifying elements into sets can take a large number of different forms, according to: (a) the type of ...

Whenever you have a problem with more than two variables to sort out, multivariate analysis can offer you an answer. It's used in a variety of fields that require the examination of statistical data, ...

The goal of this talk is to familiarize those in attendance with some common multivariate methods, such as principal component analysis, factor analysis, Hotelling’s T 2, etc. We’ll try to motivate ...

Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variable under consideration. Multivariate analysis techniques may be used for several ...

In multivariate recurrent event data regression, observation of recurrent events is usually terminated by other events that are associated with the recurrent event processes, resulting in informative ...

Customer perceptions of your company's brand are complex and difficult to predict because of the variety of factors involved. Multivariate analysis uses statistical tools such as multiple regression ...

Professor Klaus Nordhausen develops modern multivariate statistical methods to analyze high-dimensional and large datasets in different fields.

Our research group develops modern and efficient multivariate statistical methods tailored for different types of multivariate data, such as time series, spatial data, spatio-temporal data, or ...