A pioneering monograph on tensor methods applied to distributional problems arising in statistics, this work constitutes a valuable reference for graduate students and professional statisticians. Prerequisites include some knowledge of linear algebra, eigenvalue decompositions, and linear models as well as likelihood functions and likelihood ratio statistics.Index notation is the favored mode of expression throughout the book. The first chapter introduces a number of aspects of inde...

The first half of the book is aimed at quantitative research workers in biology, medicine, ecology and genetics. The book as a whole is aimed at graduate students in statistics, biostatistics, and other quantitative disciplines. Ten detailed examples show how the author approaches real-world statistical problems in a principled way that allows for adequate compromise and flexibility. The need to accommodate correlations associated with space, time and other relationships is a recurring the...