Statistical and Computational Inverse Problems

Classification Without Interaction”), and 13 (“Two-Way Crossed Classification With Interaction”). Every chapter contains two or more numerical example with the exception of Chapters 14 (“Three-Way and Higher-Order Crossed Classifications”) and 17 (“General r-Way Nested Classification”), which only contain one example each. Examples appear in the estimation, confidence interval, and hypothesis testing sections. Distribution of estimators is only discussed for the models in Chapters 11 and 15 (“Two-Way Nested Classification”). Chapters 11, 13, 15, and 16 (“Three-Way Nested Classification”) contain information on design considerations involving unbalanced experiments. The appendixes contain basic theoretical and methodological results useful in the development of unbalanced random models as well as information on the capabilities of widely available software. Packages discussed are SAS, SPSS, BMDP, S–PLUS, GENSTAT, and BUGS. The book is well organized and focused. It contains extensive coverage on crossed and nested unbalanced models. Because of the number of topics, the depth of coverage is occasionally limited. This is only a minor issue, since there are always a substantial number of references given. The organization of the book and the presentation of the material make difficult subject matter easier to follow. The main drawback to the book is that it deals only with completely random univariate models. Given the volume of information in the book, however, this is understandable. The authors point out this shortcoming in the Preface and suggest that a future work covering these topics may be forthcoming. For the application-oriented practitioner, a small disadvantage is that a number of the estimation approaches discussed, while interesting, cannot be found in the more commonly used statistical software packages. Regardless, the book makes an excellent resource for anyone working with unbalanced random models.