Linear Models: An Integrated Approach

This book has the following special features:

• Use of simple statistical ideas such as linear zero functions and covariance adjustment to explain the fundamental as well as advanced concepts
• Emphasis on the statistical interpretation of complex algebraic results
• A thorough treatment of the singular linear model, including the case of multivariate response
• A unified discussion on models with a partially unknown dispersion matrix, including mixed-effects/variance-components models and models for spatial and time series data
• Insight into updates on the linear model and their connection with diagnostics, design, variable selection, the Kalman filter, etc.
• An extensive discussion on the foundations of linear inference, along with linear alternatives to least squares
• Coverage of other special topics, such as collinearity, stochastic and inequality constraints, misspecified models, etc.
• Simpler proofs of numerous known results
• Pointers to current research through examples and exercises