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  • 1
    UID:
    almahu_9947363014102882
    Format: XV, 248 p. , online resource.
    ISBN: 9781461213345
    Series Statement: Statistics for Industry and Technology
    Content: Censored sampling arises in a life-testing experiment whenever the experimenter does not observe (either intentionally or unintentionally) the failure times of all units placed on a life-test. Inference based on censored sampling has been studied during the past 50 years by numerous authors for a wide range of lifetime distributions such as normal, exponential, gamma, Rayleigh, Weibull, extreme value, log-normal, inverse Gaussian, logistic, Laplace, and Pareto. Naturally, there are many different forms of censoring that have been discussed in the literature. In this book, we consider a versatile scheme of censoring called progressive Type-II censoring. Under this scheme of censoring, from a total of n units placed on a life-test, only m are completely observed until failure. At the time of the first failure, Rl of the n - 1 surviving units are randomly withdrawn (or censored) from the life-testing experiment. At the time of the next failure, R2 of the n - 2 -Rl surviving units are censored, and so on. Finally, at the time of the m-th failure, all the remaining Rm = n - m -Rl - . . . - Rm-l surviving units are censored. Note that censoring takes place here progressively in m stages. Clearly, this scheme includes as special cases the complete sample situation (when m = nand Rl = . . . = Rm = 0) and the conventional Type-II right censoring situation (when Rl = . . . = Rm-l = 0 and Rm = n - m).
    Note: 1 Introduction -- 1.1 The Big Picture -- 1.2 Genesis -- 1.3 The Need for Progressive Censoring -- 1.4 A Relatively Unexplored Idea -- 1.5 Mathematical Notations -- 1.6 A Friendly Note -- 2 Mathematical Properties of Progressively Type-II Right Censored Order Statistics -- 2.1 General Continuous Distributions -- 2.2 The Exponential Distribution: Spacings -- 2.3 The Uniform Distribution: Ratios -- 2.4 The Pareto Distribution: Ratios -- 2.5 Bounds for Means and Variances -- 3 Simulational Algorithms -- 3.1 Introduction -- 3.2 Simulation Using the Uniform Distribution -- 3.3 Simulation Using the Exponential Distribution -- 3.4 General Progressively Type-II Censored Samples -- 4 Recursive Computation and Algorithms -- 4.1 Introduction -- 4.2 The Exponential Distribution -- 4.3 The Doubly Truncated Exponential Distribution -- 4.4 The Pareto Distribution and Truncated Forms -- 4.5 The Power Function Distribution and Truncated Forms -- 5 Alternative Computational Methods -- 5.1 Introduction -- 5.2 Formulas in Terms of Moments of Usual Order Statistics -- 5.3 Formulas in the Case of Symmetric Distributions -- 5.4 Other Relations for Moments -- 5.5 First-Order Approximations to the Moments -- 6 Linear Inference -- 6.1 One-Parameter (Scale) Models -- 6.2 Two-Parameter (Location-Scale) Models -- 6.3 Best Linear Invariant Estimation -- 7 Likelihood Inference: Type-I and Type-II Censoring -- 71. Introduction -- 7.2 General Continuous Distributions -- 7.3 Specific Continuous Distributions -- 8 Linear Prediction -- 8.1 Introduction -- 8.2 The Exponential Case -- 8.3 Case of General Distributions -- 8.4 A Simple Approach Based on BLUEs -- 8.5 First-Order Approximations to BLUPs -- 8.6 Prediction Intervals -- 8.7 Illustrative Examples -- 9 Conditional Inference -- 9.1 Introduction -- 9.2 Inference for Location and Scale Parameters -- 9.3 Inference for Quantiles and Reliability and Prediction Intervals -- 9.4 Results for Extreme Value Distribution -- 9.5 Results for Exponential Distribution -- 9.6 Illustrative Examples -- 9.7 Results for Pareto Distribution -- 10 Optimal Censoring Schemes -- 10.1 Introduction -- 10.2 The Exponential Distribution -- 10.3 The Normal Distribution -- 10.4 The Extreme Value Distribution -- 10.5 The Extreme Value (II) Distribution -- 10.6 The Log-Normal Distribution -- 10.7 Tables -- 11 Acceptance Sampling Plans -- 11.1 Introduction -- 11.2 The Exponential Distribution -- 11.3 The Log-Normal Distribution -- Author Index.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9781461270997
    Language: English
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  • 2
    UID:
    gbv_1655485938
    Format: Online-Ressource (XV, 248 p, online resource)
    ISBN: 9781461213345
    Series Statement: Statistics for Industry and Technology
    Content: This new book offers a thorough 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). Progressive Censoring first introduces progressive sampling foundations, then discusses various properties of progressive samples. It also describes how to make exact or approximate inferences for the different statistical models with samples based on progressive censoring schemes. With many concrete examples, the book points out the greater efficiency gained by using this scheme instead of classical right-censoring methods
    Additional Edition: ISBN 9781461270997
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781461270997
    Language: English
    URL: Volltext  (lizenzpflichtig)
    Library Location Call Number Volume/Issue/Year Availability
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