Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate ...

Let ${\mathrm{Z}}_{{\mathrm{M}}_{1}\times \mathrm{N}}={\mathrm{T}}^{\frac{1}{2}}\mathrm{X}$ where (T½)2 = T is a positive definite matrix and X consists of ...

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 ...

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 semiconductor manufacturing, especially in electrical test data, but also in other parameters, there are often sets of parameters that are very highly correlated. Even a change in the correlation ...

This work presents an application of the EM-algorithm to two problems of estimation and testing in a multivariate normal distribution with missing data. The assumptions are that the observations are ...

Before we learn how to perform multivariate regression in Excel, it is important to have a refresher on regression as a whole and multivariate regression in particular. One of the hallmarks of human ...

This course is available on the MPhil/PhD in Environmental Economics, MPhil/PhD in International Relations, MPhil/PhD in Management - Information Systems and Innovation, MPhil/PhD in Social Policy, ...