Statistical Inference for Models with Multivariate T-distributed Errors

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Uniquely presents systematic analytical results using Student's t-distributed errors in linear models Statistical Inference for Models with Multivariate t-Distributed Errors presents a wide array of applications for the analysis of multivariate observations and emphasizes the Student's t-distribution method. The book illustrates the development of linear statistical models with applications to a variety of fields including mathematics, statistics, biostatistics, engineering, and the physical sciences. The book begins with a summary of the results under normal theory and proceeds to the statistical analysis of location models, simple regression, analysis of variance (ANOVA), parallelism, multiple regression, ridge regression, multivariate and simple multivariate linear models, and linear prediction. Providing a clear and balanced introduction to statistical inference, the bookalso features: * A unique connection to normal distribution, Bayesian analysis, prediction problems, and Stein shrinkage estimation * Practical real-world examples that address linear regression models with non-normal errors with practical real-world examples * Plentiful applications and end-of-chapter problems that enhance the applications for the analysis of multivariate observations * An up-to-date bibliography featuring the latest trends and advances to provide a collective resource for research Statistical Inference for Models with Multivariate t-Distributed Errors is an excellent upper-undergraduate and graduate-level textbook for courses in multivariate analysis, regression, linear models, and Bayesian analysis. The book is also a useful resource for statistical practitioners who need solid methodology within mathematical and quantitative statistics.