Accelerated Life Models: Modeling and Statistical Analysis

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$145.95
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ISBN 9781584881865
Cat# C1860
 

Features

  • Presents an important new class of models for survival analysis
  • Discusses in depth the methods of semi-parametric estimation and models with time-varying explanatory variables
  • Presents plans of experiments and methods of analyzing accelerated life data when the failure-time distribution is completely unknown and the link functions are not parameterized
  • Examines the analysis of accelerated life data when the production process is unstable
  • Includes goodness-of-fit tests for the most important models
  • Summary

    The authors of this monograph have developed a large and important class of survival analysis models that generalize most of the existing models. In a unified, systematic presentation, this monograph fully details those models and explores areas of accelerated life testing usually only touched upon in the literature.

    Accelerated Life Models: Modeling and Statistical Analysis presents models, methods of data collection, and statistical analysis for failure-time regression data in accelerated life testing and for degradation data with explanatory variables. In addition to the classical results, the authors devote considerable attention to models with time-varying explanatory variables and to methods of semiparametric estimation. They also examine the simultaneous analysis of degradation and failure-time data when the intensities of failure in different modes depend on the level of degradation and the values of explanatory variables.

    The authors avoid technical details by explaining the ideas and referring to resources where thorough analysis can be found. Whether used for teaching, research or general reference, Accelerated Life Models: Modeling and Statistical Analysis provides new and known models and modern methods of accelerated life data analysis.

    Table of Contents

    Failure Time Distributions
    Introduction
    Parametric Classes of Failure Time Distributions
    Accelerated Life Models
    Introduction
    Generalized Sedyakin's Model
    Accelerated Failure Time Model
    Proportional Hazards Model
    Generalized Proportional Hazards Models
    Generalized Additive and Additive-Multiplicative Hazards Models
    Changing Shape and Scale Models
    Generalizations
    Models Including Switch-Up and Cycling Effects
    Heredity Hypothesis
    Summary
    Accelerated Degradation Models
    Introduction
    Degradation Models
    Modeling the Influence of Explanatory Variables on Degradation
    Modeling the Traumatic Event Process
    Maximum Likelihood Estimation for FTR Data
    Censored Failure Time Data
    Parametric Likelihood Function for Right Censored FTR Data
    Score Function
    Asymptotic Properties of the Maximum Likelihood Estimators
    Approximate Confidence Intervals
    Some Remarks on Semi-Parametric Estimation
    AFT Model: Parametric FTR and ALT Data Analysis
    Parametrization of the AFT Model
    Interpretation of the Regression Coefficients
    FTR Data Analysis: Scale-Shape Families of Distributions
    FTR Data Analysis: Generalized Weibull Distribution
    FTR Data Analysis: Exponential Distribution
    Plans of Experiments in Accelerated Life Testing
    Parametric Estimation in ALT Under the AFT Model
    AFT Models: Semi-Parametric FTR and AFT Data Analysis
    FTR Data Analysis
    Semi-Parametric Estimation in ALT
    PH Model: Semi-Parametric FTR Data Analysis
    Introduction
    Parametrization of the PH Model
    Interpretation of the Regression Coefficients
    Semi-Parametric FTR Data Analysis for the PH Model
    GPH Models: FTR Analysis
    Introduction
    Semi-Parametric FTR Data Analysis for the GPH1 Models
    Semi-Parametric FTR Data Analysis: Intersecting Hazards
    Changing Scale and Shape Model
    Parametric FTR Data Analysis
    Semi-Parametric FTR Data Analysis
    Semi-Parametric Estimation in ALT
    GAH and GAMH Model: Semi-Parametric FTR and ALT Data Analysis
    GAH Model
    GAMH Model
    AAR Model
    PPAR Model
    Estimation When a Process of Production in Unstable
    Application of the AFT Model
    Application of the GPH1 Model
    Goodness-of-Fit for Accelerated Life Models
    Goodness-of-Fit for the GS Model
    Goodness-of-Fit for the Model with Absence of Memory
    Goodness-of-Fit for the AFT Model
    Goodness-of-Fit for the PH Model
    Goodness-of-Fit for the GPH Models
    Goodness-of-Fit for the Parametric Regression Models
    Estimation in Degradation Models with Explanatory Variables
    Introduction
    Linear Path Models
    Gamma and Shock Processes
    Some Results from Stochastic Process Theory
    Stochastic Process. Filtration
    Counting Process
    Stochastic Integral
    Conditional Expectation
    Martingale
    Predictable Process and Doob-Meyer Decomposition
    Predictable Variation and Predictable Covariation
    Stochastic Integrals with Respect to Martingales
    Localization
    Stochastic Integrals with Respect to Martingales (continuation)
    Weak Convergence
    Central Limit Theorem for Martingales
    Non-Parametric Estimators of the Cumulative Hazard and the Survival Function
    Product-Integral
    Delta Method
    References

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