This new volume offers a throughout guide to the theory and methods of progressive censoring for practitioners and professionals in applied statistics, quality control, life testing and reliability testing. In many industrial experiments involving lifetimes of machines or units, experiments have to be terminated early due to a variety of circumstances. Samples that arise from such experiments are called censored samples, and a new efficient alternative method is referred to as "progressive censoring" (where the removal of live units at time of failure is employed). This book first introduces progressive sampling foundations, then discusses various properties of progressive samples. It also describes how to make exact or approximate inferences for the difference statistical models with samples based on progressive censoring schemes. With many concrete examples, the book points out the greater efficiency gained by suing this scheme instead of classical right-censorship methods. With its accessible style and examples, the book should be an essential resource for progressive censoring methodology.Advanced students, practitioners, and professionals in applied statistics, reliability and life-testing research should find it a useful guide to understanding and using this new methodology in their work.

This new volume offers a throughout guide to the theory and methods of progressive censoring for practitioners and professionals in applied statistics, quality control, life testing and reliability testing. In many industrial experiments involving lifetimes of machines or units, experiments have to be terminated early due to a variety of circumstances. Samples that arise from such experiments are called censored samples, and a new efficient alternative method is referred to as "progressive censoring" (where the removal of live units at time of failure is employed). This book first introduces progressive sampling foundations, then discusses various properties of progressive samples. It also describes how to make exact or approximate inferences for the difference statistical models with samples based on progressive censoring schemes. With many concrete examples, the book points out the greater efficiency gained by suing this scheme instead of classical right-censorship methods. With its accessible style and examples, the book should be an essential resource for progressive censoring methodology.Advanced students, practitioners, and professionals in applied statistics, reliability and life-testing research should find it a useful guide to understanding and using this new methodology in their work.