Name: Progressive Censoring By N. Balakrishnan
SKU: 9780817640019
Price: 370.49 AUD
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Progressive Censoring Summary

Progressive Censoring: Theory, Methods, and Applications by N. Balakrishnan

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.

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.- 2.1.1 Introduction.- 2.1.2 Results.- 2.2 The Exponential Distribution: Spacings.- 2.2.1 Introduction.- 2.2.2 Progressively Type-II Right Censored Spacings.- 2.2.3 Deriving Moments Using Independent Spacings.- 2.3 The Uniform Distribution: Ratios.- 2.3.1 Introduction.- 2.3.2 Independent Ratios.- 2.3.3 Deriving Moments Using Independent Ratios.- 2.4 The Pareto Distribution: Ratios.- 2.4.1 Introduction.- 2.4.2 Independent Ratios.- 2.4.3 Deriving Moments Using Independent 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.- 3.4.1 Arbitrary Continuous Distributions.- 3.4.2 The Exponential Distribution.- 3.4.3 The Uniform Distribution.- 4 Recursive Computation and Algorithms.- 4.1 Introduction.- 4.2 The Exponential Distribution.- 4.2.1 Recurrence Relations for Single Moments.- 4.2.2 Recurrence Relations for Product Moments.- 4.2.3 Recursive Algorithm.- 4.3 The Doubly Truncated Exponential Distribution.- 4.3.1 Recurrence Relations for Single Moments.- 4.3.2 Recurrence Relations for Product Moments.- 4.3.3 Recursive Algorithm.- 4.4 The Pareto Distribution and Truncated Forms.- 4.4.1 Recurrence Relations for Single Moments.- 4.4.2 Recurrence Relations for Product Moments.- 4.4.3 Recursive Algorithm.- 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.- 5.3.1 Progressive Withdrawal.- 5.3.2 Properties of Progressively Type-II Left Withdrawn Order Statistics.- 5.3.3 Moments of Progressively Type-II Right Censored Order Statistics from 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.- 6.1.1 Introduction.- 6.1.2 The Exponential Distribution.- 6.1.3 The Uniform Distribution.- 6.1.4 The Pareto Distribution.- 6.1.5 First-Order Approximation to the BLUE.- 6.2 Two-Parameter (Location-Scale) Models.- 6.2.1 Introduction.- 6.2.2 The Exponential Distribution.- 6.2.3 The Uniform Distribution.- 6.2.4 The Pareto Distribution.- 6.2.5 The Laplace Distribution.- 6.2.6 The Extreme Value Distribution.- 6.2.7 First-Order Approximations to the BLUEs.- 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.- 7.3.1 The Normal Distribution.- 7.3.2 The Exponential Distribution.- 7.3.3 The Weibull Distribution.- 7.3.4 The Uniform Distribution.- 7.3.4 The Pareto Distribution.- 7.3.6 The Laplace Distribution.- 7.3.7 Other Distributions (Log-Normal, Gamma, Burr).- 8 Linear Prediction.- 8.1 Introduction.- 8.2 The Exponential Case.- 8.3 Case of General Distributions.- 8.3.1 Scale-Parameter Distributions.- 8.3.2 Location-Scale 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.- 9.3.1 Inference for Quantiles.- 9.3.2 Inference for Reliability.- 9.3.3 Prediction Intervals for Future Failures.- 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.- 10.3.1 Discussion of Results.- 10.4 The Extreme Value Distribution.- 10.4.1 Discussion of Results.- 10.5 The Extreme Value (II) Distribution.- 10.5.1 Discussion of Results.- 10.6 The Log-Normal Distribution.- 10.6.1 Discussion of Results.- 10.7 Tables.- 11 Acceptance Sampling Plans.- 11.1 Introduction.- 11.2 The Exponential Distribution.- 11.2.1 One-Sided Sampling Plans.- 11.2.2 Two-Sided Sampling Plans.- 11.3 The Log-Normal Distribution.- Author Index.

Additional information

Sku

NPB9780817640019

ISBN 13

9780817640019

ISBN 10

0817640010

Title

Progressive Censoring: Theory, Methods, and Applications by N. Balakrishnan

Author

N. Balakrishnan

Series

Statistics for Industry and Technology

Condition

New

Binding type

Hardback

Publisher

Birkhauser Boston Inc

Year published

2000-06-16

Number of pages

248

Prizes

N/A

Cover note

Book picture is for illustrative purposes only, actual binding, cover or edition may vary.

Note

This is a new book - be the first to read this copy. With untouched pages and a perfect binding, your brand new copy is ready to be opened for the first time

Progressive Censoring N. Balakrishnan

Progressive Censoring by N. Balakrishnan

Summary

Progressive Censoring Summary

Progressive Censoring: Theory, Methods, and Applications by N. Balakrishnan

Table of Contents

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