Books in the Springer Series in Statistics series

The statistical analysis of discrete multivariate data has received a great deal of attention in the statistics literature over the past two decades.

Now available in paperback, this book covers some recent developments in statistical inference. It provides methods applicable in problems involving nuisance parameters such as those encountered in comparing two exponential distributions or in ANOVA without the assumption of equal error variances.

Previous edition sold over 1400 copies worldwide. This new edition includes many more real-world illustrations from biology, business, clinical trials, economics, geology, law, medicine, social science and engineering along with twice the number of exercises.

However, a modeling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until recently. More advanced topics are covered in Part 3, including the mathematical properties of the models and extensions of the models for specific problems.

Originally published in 1981, this title includes a bibliography and an errata list.

Now available in paperback, this book provides a comprehensive account of survey sampling theory and methodology suitable for students and researchers across a variety of disciplines. The first textbook that systematically extends traditional sampling theory with the aid of a modern model assisted outlook.

This book contains the ideas of functional data analysis by a number of case studies. Every reader should gain not only a specific understanding of the methods of functional data analysis, but more importantly a general insight into the underlying patterns of thought.

Introduces the basic concepts and methods useful in the analysis and modeling of multivariate time series data that may arise in business and economics, engineering, geophysical sciences, and other fields. This book concentrates on the time-domain analysis of multivariate time series, and assumes a background in univariate time series analysis.