This book offers a thorough and updated guide to the theory and methods of progressive censoring, an area that has experienced tremendous growth over the last decade. The theory has developed quite nicely in some special cases having practical applications to reliability and quality. The Art of Progressive Censoring is a valuable reference for graduate students, researchers, and practitioners in applied statistics, quality control, life testing, and reliability. With its accessible style and concrete examples, the work may also 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.

This new book offers a guide to the theory and methods of progressive censoring. In many industrial experiments involving lifetimes of machines or units, experiments have to be terminated early. Progressive Censoring first introduces progressive sampling foundations, and then discusses various properties of progressive samples. The book points out the greater efficiency gained by using this scheme instead of classical right-censoring methods.

Censored sampling arises in a life testing experiment whenever the experimenter does not observe the failure times of all items placed on a life test. Progressive censoring scheme is useful in both industrial life testing applications and clinical settings; it allows the removal of surviving experimental units before the termination of the test. In this book, we obtain the maximum likelihood, and Bayes estimations for the parameter of the Burr-X model as well as the binomial parameter, based on progressive first-failure censoring with binomial removals. Bayes estimators under symmetric and asymmetric loss functions are obtained. Three special cases from this censoring scheme have been considered. Farther, we discuss the problem of predicting future record values and ordinary order statistics from Burr-X model based on progressively type-II censored with random removals, were the number of units removed at each failure time has a discrete binomial distribution. We use the Bayes procedure to derive both point and interval prediction. The maximum likelihood prediction both point and interval using "plug-in" procedure for future record values and ordinary order statistics are derived.

This book deals with the mathematical aspects of survival analysis and reliability as well as other topics, reflecting recent developments in the following areas: applications in epidemiology; probabilistic and statistical models and methods in reliability; models and methods in survival analysis, longevity, aging, and degradation; accelerated life models; quality of life; new statistical challenges in genomics. The work will be useful to a broad interdisciplinary readership of researchers and practitioners in applied probability and statistics, industrial statistics, biomedicine, biostatistics, and engineering.

Censoring is very common in life testing experiments and reliability studies. Progressive first-failure-censoring and an adaptive progressive Type II censoring schemes will be a good choice in this situation. Also, record values and associated statistics are of great importance in several real life problems. There are a number of situations in which an observation is retained only if it is a record value. In this book, we propose different methods to estimate the parameters of the Burr-XII distribution using different censoring schemes and record values. We used the maximum likelihood estimator, different parametric bootstrap methods and we provide a Bayesian method to estimate these parameters as well as the coefficient of variation, the stress-strength reliability model and hazard functions. In the Bayesian method we propose two approaches to approximate the posterior: Lindley's approximation and the Markov chain Monte Carlo (MCMC) methods. Also, the statistical Bayesian predictions have been treated. Bayesian prediction intervals based on progressive first-failure-censored from Burr-XII as a formative sample are obtained and discussed