The Art of Progressive Censoring

This is an up-to-date guide to the theory and methods of progressive censoring. Key topics include the foundations of progressive censoring; properties of progressively censored samples; and newly simulated data sets illustrating concepts throughout the text.

This monograph offers a thorough and updated guide to the theory and methods of progressive censoring, an area that has experienced tremendous growth in recent years. Progressive censoring, originally proposed in the 1950s, is an efficient method of handling samples from industrial experiments involving lifetimes of units that have either failed or censored in a progressive fashion during the life test, with many practical applications to reliability and quality.

Key topics and features:

• Data sets from the literature as well as newly simulated data sets are used to illustrate concepts throughout the text
• Emphasis on real-life applications to life testing, reliability, and quality control
• Discussion of parametric and nonparametric inference

• Coverage of experimental design with optimal progressive censoring
  The Art of Progressive Censoring is a valuable reference for graduate students, researchers, and practitioners inapplied statistics, quality control, life testing, and reliability. With its accessible style and concrete examples, the work may also be used as a textbook in advanced undergraduate or beginning graduate courses on censoring or progressive censoring, as well as a supplementary textbook for a course on ordered data.

  A thorough and updated guide to the theory and methods of progressive censoring, which has seen tremendous growth over the last decade Emphasis on real-life applications to life testing, reliability, and quality control Numerous examples and illustrations demonstrate the benefits of progressive censoring May be used as a textbook in an advanced undergraduate or a beginning graduate course on censoring or progressive censoring, as well as a supplementary textbook for a course on ordered data Includes supplementary material: sn.pub/extras

  Autorentext
  N. Balakrishnan is an Associate Director and Professor at Department of Aerospace Engineering and Supercomputer Edu- cation and Research Centre, Indian Institute of Science. His research interests include numerical electromagnetic, multi-parameter radars, and signal processing. His publications include 19 books and many peer-reviewed journal papers.

  Inhalt
  Part I: Distribution Theory and Models.- Progressive censoring: Data and models.- Progressive Type-II censoring: Distribution theory.- Further distributional results on progressive Type-II censoring.- Progressive Type-I censoring: Basic properties.- Progressive hybrid censoring: Distributions and properties.- Adaptive progressive Type-II censoring and related models.- Moments of progressively Type-II censored order statistics.- Simulation of progressively censored order statistics.- Information Measures.- Progressive Type-II censoring under non-standard conditions.- Part II: Inference.- Linear estimation in progressive Type-II censoring.- Maximum likelihood estimation in progressive Type-II censoring.- Point estimation in progressive Type-I censoring.- Progressive hybrid and adaptive censoring and related inference.- Bayesian inference for progressively Type-II censored data.- Point prediction from progressively Type-II censored samples.- Statistical intervals for progressively Type-IIcensored data.- Progressive Type-I interval censored data.- Goodness-of-fit-tests in progressive Type-II censoring.- Counting and quantile processes and progressive censoring.- Nonparametric inferential issues in progressive Type-II censoring.- Part III: Applications in Survival Analysis and Reliability.- Acceptance sampling plans.- Accelerated life-testing.- Stress-strength models with progressively censored data.- Multi-sample models.- Optimal experimental designs.- Part IV: Appendices.- Appendix A: Distributions.- Appendix B: Additional demonstrative datasets.- Notation.- References.- Author index.- Subject index.