Statistical models and methods for reliability and survival analysis / edited by Vincent Couallier.

Format:

Book

Contributor:

Couallier, Vincent.

Series:

Mathematics and statistics series (ISTE)

Mathematics and Statistics Series

Language:

English

Subjects (All):

Survival analysis (Biometry).

Reliability (Engineering)--Statistical methods.

Reliability (Engineering).

Physical Description:

1 online resource (433 p.)

Edition:

1st ed.

Place of Publication:

London, England ; Hoboken, New Jersey : ISTE Ltd : John Wiley & Sons, 2014.

Language Note:

English

Summary:

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Stati

Contents:

Cover; Title page; Table of Contents; Preface; Biography of Mikhail Stepanovitch Nikouline; PART 1. STATISTICAL MODELS AND METHODS; Chapter 1. Unidimensionality, Agreement and Concordance Probability; 1.1. Introduction; 1.2. From reliability to unidimensionality: CAC and curve; 1.2.1. Classical unidimensional models for measurement; 1.2.2. Reliability of an instrument: CAC; 1.2.3. Unidimensionality of an instrument: BRC; 1.3. Agreement between binary outcomes: the kappa coefficient; 1.3.1. The kappa model; 1.3.2. The kappa coefficient; 1.3.3. Estimation of the kappa coefficient

1.4. Concordance probability1.4.1. Relationship with Kendall's τ measure; 1.4.2. Relationship with Somer's D measure; 1.4.3. Relationship with ROC curve; 1.5. Estimation and inference; 1.6. Measure of agreement; 1.7. Extension to survival data; 1.7.1. Harrell's c-index; 1.7.2. Measure of discriminatory power; 1.8. Discussion; 1.9. Bibliography; Chapter 2. A Universal Goodness-of-Fit Test Based on Regression Techniques; 2.1. Introduction; 2.2. The Brain and Shapiro procedure for the exponential distribution; 2.3. Applications of the Brain and Shapiro test

2.4. Small sample null distribution of the test statistic for specific distributions2.5. Power studies; 2.6. Some real examples; 2.7. Conclusions; 2.8. Acknowledgment; 2.9. Bibliography; Chapter 3. Entropy-type Goodness-of-Fit Tests for Heavy-Tailed Distributions; 3.1. Introduction; 3.2. The entropy test for heavy-tailed distributions; 3.2.1. Development and asymptotic theory; 3.2.2. Discussion; 3.3. Simulation study; 3.4. Conclusions; 3.5. Bibliography; Chapter 4. Penalized Likelihood Methodology and Frailty Models; 4.1. Introduction

4.2. Penalized likelihood in frailty models for clustered data4.2.1. Gamma distributed frailty; 4.2.2. Inverse Gaussian distributed frailty; 4.2.3. Uniform distributed frailty; 4.3. Simulation results; 4.4. Concluding remarks; 4.5. Bibliography; Chapter 5. Interactive Investigation of Statistical Regularities in Testing Composite Hypotheses of Goodness of Fit; 5.1. Introduction; 5.2. Distributions of the test statistics in the case of testing composite hypotheses; 5.3. Testing composite hypotheses in ""real-time""; 5.4. Conclusions; 5.5. Acknowledgment; 5.6. Bibliography

Chapter 7. Within the Sample Comparison of Prediction Performance of Models and Submodels: Application to Alzheimer's Disease

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

9781118826805

1118826809

9781118826997

111882699X

9781118827161

1118827163

OCLC:

866442600