Usually ships in 2-3 days. The purpose of this volume is to use three fundamental ideas from linear model theory and exploit their properties in examining multivariate, time series and spatial data. Multivariate linear models are viewed as linear models with a nondiagonal covariance matrix. Discriminant analysis is related to the Mahalanobis distance and multivariate analysis of variance.
Principle components are best linear predictors.
Frequency domain time series involves linear models with a peculiar design matrix. Time domain analysis involves models that are linear in the parameters but have random design matrices. Best linear predictors are used for forecasting time series and for estimation in time domain analysis. Spatial data analysis involves linear models in which the covariance matrix is modeled from the data and making best linear unbiased predictions of future observables. This book develops a unified approach to this wide ranging collection of problems.