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Author Couallier, Vincent
Title Statistical Models and Methods for Reliability and Survival Analysis
Imprint Somerset : John Wiley & Sons, Incorporated, 2013
©2014
book jacket
Edition 1st ed
Descript 1 online resource (433 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Iste
Iste
Note 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 probability -- 1.4.1. 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 distributions -- 2.5. 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 data
4.2.1. 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 6. Modeling of Categorical Data -- 6.1. Introduction -- 6.2. Continuous conditional distributions -- 6.2.1. Conditional normal distribution -- 6.2.1.1. Estimation of parameters -- 6.2.2. More general continuous conditional distributions -- 6.2.2.1. Conditional distribution -- 6.2.2.2. Normal copula -- 6.3. Discrete conditional distributions -- 6.3.1. Parametric conditional distributions -- 6.3.2. Estimation of parameters -- 6.4. Goodness of fit -- 6.4.1. Distribution of ˆX2 -- 6.5. Modeling of categorical data -- 6.5.1. Contingency tables -- 6.5.1.1. General tables -- 6.5.1.2. Further examples -- 6.6. Bibliography -- Chapter 7. Within the Sample Comparison of Prediction Performance of Models and Submodels: Application to Alzheimer's Disease -- 7.1. Introduction -- 7.2. Framework -- 7.2.1. General description of the data set and the models to be compared -- 7.2.2. Definition of the performance prediction criteria: IDI and BRI -- 7.3. Estimation of IDI and BRI -- 7.3.1. General estimating equations for IDI and BRI -- 7.3.2. Estimation of IDI and BRI in the logistic case -- 7.3.2.1. Asymptotics of IDI2/1 for logistic predictors -- 7.3.2.2. Asymptotics of BRI2/1 for logistic predictors -- 7.4. Simulation studies -- 7.4.1. First simulation -- 7.4.2. Second simulation: Gu and Pepe's example
7.5. The three city study of Alzheimer's disease -- 7.6. Conclusion -- 7.7. Bibliography -- Chapter 8. Durbin-Knott Components and Transformations of the Cramér-von Mises Test -- 8.1. Introduction -- 8.2. Weighted Cramér-von Mises statistic -- 8.3. Examples of the Cramér-von Mises statistics -- 8.3.1. Classical Cramér-von Mises statistic -- 8.3.2. Anderson-Darling statistic -- 8.3.3. Cramér-von Mises statistic with the power weight function -- 8.4. Weighted parametric Cramér-von Mises statistic -- 8.4.1. Covariance functions of weighted parametric empirical process -- 8.4.2. Eigenvalues and eigenfunctions for weighted parametric Cramérvon Mises statistic -- 8.5. Transformations of the Cramér-von Mises statistic -- 8.5.1. Preliminary notes -- 8.5.2. Replacement of eigenvalues -- 8.5.3. Transformed statistics -- 8.6. Bibliography -- Chapter 9. Conditional Inference in Parametric Models -- 9.1. Introduction and context -- 9.2. The approximate conditional density of the sample -- 9.2.1. Approximation of conditional densities -- 9.2.2. The proxy of the conditional density of the sample -- 9.2.3. Comments on implementation -- 9.3. Sufficient statistics and approximated conditional density -- 9.3.1. Keeping sufficiency under the proxy density -- 9.3.2. Rao-Blackwellization -- 9.4. Exponential models with nuisance parameters -- 9.4.1. Conditional inference in exponential families -- 9.4.2. Application of conditional sampling to MC tests -- 9.4.2.1. Context -- 9.4.2.2. Bimodal likelihood: testing the mean of a normal distribution in dimension -- 9.4.3. Estimation through conditional likelihood -- 9.5. Bibliography -- Chapter 10. On Testing Stochastic Dominance by Exceedance, Precedence and Other Distribution-Free Tests, with Applications -- 10.1. Introduction -- 10.2. Results -- 10.2.1. The experimental data set
10.2.2. An application of the Wilcoxon-Mann-Whitney statistics -- 10.2.3. One-sided Kolmogorov-Smirnov tests -- 10.2.4. Precedence and Exceedance Tests -- 10.3. Negative binomial limit laws -- 10.4. Conclusion -- 10.5. Bibliography -- Chapter 11. Asymptotically Parameter-Free Tests for Ergodic Diffusion Processes -- 11.1. Introduction -- 11.2. Ergodic diffusion process and some limits -- 11.3. Shift parameter -- 11.4. Shift and scale parameters -- 11.5. Bibliography -- Chapter 12. A Comparison of Homogeneity Tests for Different Alternative Hypotheses -- 12.1. Homogeneity tests -- 12.1.1. Tests for data without censoring -- 12.1.2. Tests for data with censoring -- 12.2. Alternative hypotheses -- 12.3. Power simulation -- 12.3.1. Power of tests without censoring -- 12.3.2. Power of tests with censoring -- 12.3.2.1. How does the distribution of censoring time affect the power of the test? -- 12.3.2.2. How does the censoring rate affect the power of the test? -- 12.4. Statistical inference -- 12.5. Acknowledgment -- 12.6. Bibliography -- Chapter 13. Some Asymptotic Results for Exchangeably Weighted Bootstraps of the Empirical Estimator of a Semi-Markov Kernel with Applications -- 13.1. Introduction -- 13.2. Semi-Markov setting -- 13.3. Main results -- 13.4. Bootstrap for a multidimensional empirical estimator of a continuoustime semi-Markov kernel -- 13.5. Confidence intervals -- 13.6. Bibliography -- Chapter 14. On Chi-Squared Goodness-of-Fit Test for Normality -- 14.1. Chi-squared test for normality -- 14.2. Simulation study -- 14.3. Bibliography -- PART 2. STATISTICAL MODELS AND METHODS IN SURVIVAL ANALYSIS -- Chapter 15. Estimation/Imputation Strategies for Missing Data in Survival Analysis -- 15.1. Introduction -- 15.2. Model and strategies -- 15.2.1. Model assumptions -- 15.2.2. Strategy involving knowledge of ζ
15.2.3. Strategy involving knowledge of π -- 15.2.4. Estimation of ζ or π : logit or non-parametric regression -- 15.2.5. Computing the hazard estimators -- 15.2.6. Theoretical results -- 15.3. Imputation-based strategy -- 15.4. Numerical comparison -- 15.5. Proofs -- 15.6. Bibliography -- Chapter 16. Non-Parametric Estimation of Linear Functionals of a Multivariate Distribution Under Multivariate Censoring with Applications -- 16.1. Introduction -- 16.2. Non-parametric estimation of the distribution -- 16.3. Asymptotic properties -- 16.4. Statistical applications of functionals -- 16.4.1. Dependence measures -- 16.4.2. Bootstrap -- 16.4.3. Linear regression -- 16.5. Illustration -- 16.6. Conclusion -- 16.7. Acknowledgment -- 16.8. Bibliography -- Chapter 17. Kernel Estimation of Density from Indirect Observation -- 17.1. Introduction -- 17.1.1. Random partition -- 17.1.2. Indirect observation -- 17.1.3. Kernel density estimator -- 17.2. Density of random vector Λ(X) -- 17.3. Pseudo-kernel density estimator -- 17.3.1. Pointwise density estimation based on indirect data -- 17.3.2. Bias of the kernel estimator -- 17.3.3. Estimate of variance -- 17.4. Bibliography -- Chapter 18. A Comparative Analysis of Some Chi-Square Goodness-of-Fit Tests for Censored Data -- 18.1. Introduction -- 18.2. Chi-square goodness-of-fit tests for censored data -- 18.2.1. NRR χ2 test -- 18.2.2. GPF χ2 test -- 18.3. The choice of grouping intervals -- 18.3.1. Equifrequent grouping (EFG) -- 18.3.2. Intervals with equal expected numbers of failures (EENFG) -- 18.3.3. Optimal grouping (OptG) -- 18.4. Empirical power study -- 18.5. Conclusions -- 18.6. Acknowledgment -- 18.7. Bibliography -- Chapter 19. A Non-parametric Test for Comparing Treatments with Missing Data and Dependent Censoring -- 19.1. Introduction -- 19.2. The proposed test statistic
19.3. Asymptotic distribution of the proposed test statistic
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, Statistical Models and Methods in Survival Analysis, and Reliability and Maintenance. The book is intended for researchers interested in statistical methodology and models useful in survival analysis, system reliability and statistical testing for censored and non-censored data
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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Link Print version: Couallier, Vincent Statistical Models and Methods for Reliability and Survival Analysis Somerset : John Wiley & Sons, Incorporated,c2013 9781118826805
Subject Survival analysis (Biometry);Reliability (Engineering) -- Statistical methods
Electronic books
Alt Author Gerville-Réache, Léo
Huber-Carol, Catherine
Limnios, Nikolaos
Mesbah, Mounir
Gerville-Réache, Léo
Limnios, Nikolaos
Mesbah, Mounir
Gerville-Reache, Leo
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