Advances in Degradation Modeling [electronic resource] : Applications to Reliability, Survival Analysis, and Finance /
Nikulin, M.S.editor.
Limnios, Nikolaos.editor.
Balakrishnan, N.editor.
Kahle, Waltraud.editor.
Huber-Carol, Catherine.editor.
This volume—dedicated to William Q. Meeker on the occasion of his sixtieth birthday—is a collection of invited chapters covering recent advances in accelerated life testing and degradation models. The book covers a wide range of applications to areas such as reliability, quality control, the health sciences, economics, and finance. Specific topics covered include: * Accelerated testing and inference * Step-stress testing and inference * Nonparametric inference * Model validity in accelerated testing * The point process approach * Bootstrap methods in degradation analysis * Exact inferential methods in reliability * Dynamic perturbed systems * Degradation models in statistics Advances in Degradation Modeling is an excellent reference for researchers and practitioners in applied probability and statistics, industrial statistics, the health sciences, quality control, economics, and finance.
Description based upon print version of record.
Includes bibliographical references and index.
Review, Tutorials, and Perspective -- Trends in the Statistical Assessment of Reliability -- Degradation Processes: An Overview -- Defect Initiation, Growth, and Failure – A General Statistical Model and Data Analyses -- Properties of Lifetime Estimators Based on Warranty Data Consisting only of Failures -- Shock Models -- Shock Models -- Parametric Shock Models -- Poisson Approximation of Processes with Locally Independent Increments and Semi-Markov Switching – Toward Application in Reliability -- On Some Shock Models of Degradation -- Degradation Models -- The Wiener Process as a Degradation Model: Modeling and Parameter Estimation -- On the General Degradation Path Model: Review and Simulation -- A Closer Look at Degradation Models: Classical and Bayesian Approaches -- Optimal Prophylaxis Policy Under Non-monotone Degradation -- Deterioration Processes With Increasing Thresholds -- Failure Time Models Based on Degradation Processes -- Degradation and Fuzzy Information -- A New Perspective on Damage Accumulation, Marker Processes, and Weibull’s Distribution -- Reliability Estimation and ALT -- Reliability Estimation of Mechanical Components Using Accelerated Life Testing Models -- Reliability Estimation from Failure-Degradation Data with Covariates -- Asymptotic Properties of Redundant Systems Reliability Estimators -- An Approach to System Reliability Demonstration Based on Accelerated Test Results on Components -- Survival Function Estimation -- Robust Versus Nonparametric Approaches and Survival Data Analysis -- Modelling Recurrent Events for Repairable Systems Under Worse Than Old Assumption -- Survival Models for Step-Stress Experiments With Lagged Effects -- Estimation of Density on Censored Data -- Competing Risk and Chaotic Systems -- Toward a Test for Departure of a Trajectory from a Neighborhood of a Chaotic System -- Probability Plotting with Independent Competing Risks.
This volume—dedicated to William Q. Meeker on the occasion of his sixtieth birthday—is a collection of invited chapters covering recent advances in accelerated life testing and degradation models. The book covers a wide range of applications to areas such as reliability, quality control, the health sciences, economics, and finance. Specific topics covered include: * Accelerated testing and inference * Step-stress testing and inference * Nonparametric inference * Model validity in accelerated testing * The point process approach * Bootstrap methods in degradation analysis * Exact inferential methods in reliability * Dynamic perturbed systems * Degradation models in statistics Advances in Degradation Modeling is an excellent reference for researchers and practitioners in applied probability and statistics, industrial statistics, the health sciences, quality control, economics, and finance.
Probabilities.
Statistics .
Probability Theory and Stochastic Processes.
Statistical Theory and Methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Statistics for Business, Management, Economics, Finance, Insurance.