Ranking of Multivariate Populations A Permutation Approach with Applications

Ranking of Multivariate Populations: A Permutation Approach with Applications presents a novel permutation-based nonparametric approach for ranking several multivariate populations. Using data collected from both experimental and observation studies, it covers some of the most useful designs widely applied in research and industry investigations, such as multivariate analysis of variance (MANOVA) and multivariate randomized complete block (MRCB) designs.

The first section of the book introduces the topic of ranking multivariate populations by presenting the main theoretical ideas and an in-depth literature review. The second section discusses a large number of real case studies from four specific research areas: new product development in industry, perceived quality of the indoor environment, customer satisfaction, and cytological and histological analysis by image processing. A web-based nonparametric combination global ranking software is also described.

Designed for practitioners and postgraduate students in statistics and the applied sciences, this application-oriented book offers a practical guide to the reliable global ranking of multivariate items, such as products, processes, and services, in terms of the performance of all investigated products/prototypes.

Theory and Methods: Introduction and Motivation. Permutation Tests and Nonparametric Combination Methodology. A Permutation Approach for Ranking of Multivariate Populations. Software Tools, Applications, and Case Studies: The Software Nonparametric Combination Global Ranking. Applications in New Product Development in Industry. Subjective Evaluations and Perceived Quality of the Indoor Environment. Evaluating Customer Satisfaction in Public and Private Services. Cytological and Histological Analysis by Image Processing: The Ranking Approach. Index.

AuthorsLivio Corain, PhD, is an assistant professor of statistics in the Department of Management and Engineering at the University of Padua.

Rosa Arboretti, PhD, is an assistant professor of statistics in the Department of Civil, Environmental and Architectural Engineering at the University of Padua.

Stefano Bonnini, PhD, is an assistant professor of statistics in the Department of Economics and Management at the University of Ferrara.

Contributors
Luigi Salmaso, PhD, is a full professor of statistics in the Department of Management and Engineering at the University of Padua.

Eleonora Carrozzo is a PhD student in managerial engineering in the Department of Management and Engineering at the University of Padua.