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This book discusses current methods of estimation in linear models. In particular, the authors address the methodology of linear multiple regression models that plays an important role in almost every scientific investigations in various fields, including economics, engineering, and biostatistics. The standard estimation method for regression parameters is the ordinary least square (OLS) principal where residual squared errors are minimized. Applied statisticians are often not satisfied with OLS estimators when the design matrix is ill-conditioned, leading to a multicollinearity problem...

This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models with Multivariate t-Distributed Errors

• Includes a wide array of applications for the analysis of multivariate observations
• Emphasizes the development of linear statistical models with applications to engineering, the physical sciences, and mathematics
• Contains an up-to-date bibliography featuring the latest trends and advances in the field to provide a collective source for research on the topic
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