Series Editor:
Zbynek Sidak, Mathematical Institute Academy of Sciences, Czech Republic
David Aldous
Pranab Sen, University of North Carolina, Chapel Hill, U.S.A.
By
William Moser, Worcester Polytechnic Institute
Description
Linear models, normally presented in a highly theoretical and mathematical style, are brought down to earth in this comprehensive textbook.
Linear Models examines the subject from a mean model perspective, defining simple and easy-to-learn rules for building
mean models, regression models, mean vectors, covariance matrices and sums of squares matrices for balanced and unbalanced data sets.
The author includes both applied and theoretical discussions of the multivariate normal distribution, quadratic forms, maximum likelihood
estimation, less than full rank models, and general mixed models. The mean model is used to bring all of these topics together in a coherent
presentation of linear model theory.
Included in series
Probability and Mathematical Statistics
Audience:
Graduate students in statistics
