Ian T. Jolliffe

Theory and Applications of Correspondence Analysis.

Principal Component Analysis

Introduction * Properties of Population Principal Components * Properties of Sample Principal Components * Interpreting Principal Components: Examples * Graphical Representation...

Principal Component Analysis

Abstract When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It re...

Principal Component Analysis

Principal component analysis: a review and recent developments

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, ...

A Modified Principal Component Technique Based on the LASSO

In many multivariate statistical techniques, a set of linear functions of the original p variables is produced. One of the more difficult aspects of these techniques is the inte...

In search of simple structures in climate: simplifying EOFs

Empirical orthogonal functions (EOFs) are widely used in climate research to identify dominant patterns of variability and to reduce the dimensionality of climate data. EOFs, ho...

ON RELATIONSHIPS BETWEEN UNCENTRED AND COLUMN-CENTRED PRINCIPAL COMPONENT ANALYSIS

Principal component analysis (PCA) can be seen as a singular value decomposition (SVD) of a column-centred data matrix. In a number of applications, no pre-processing of the dat...

Revised “LEPS” Scores for Assessing Climate Model Simulations and Long-Range Forecasts

The most commonly used measures for verifying forecasts or simulators of continuous variables are root-mean-squared error (rmse) and anomaly correlation. Some disadvantages of t...

Principal Component Analysis for Special Types of Data