Quantile functions provide a unique, unprecedentedly simple, robust, and precise approach to reliability theory
Broad applicability across fields such as statistics, survival analysis, economics, engineering, demography, insurance, and medical science
Clear presentation with many examples, figures, and tables
Quantile-Based Reliability Analysis presents a novel approach to reliability theory using quantile functions in contrast to the traditional approach based on distribution functions. Quantile functions and distribution functions are mathematically equivalent ways to define a probability distribution. However, quantile functions have several advantages over distribution functions. First, many data sets with non-elementary distribution functions can be modeled by quantile functions with simple forms. Second, most quantile functions approximate many of the standard models in reliability analysis quite well. Consequently, if physical conditions do not suggest a plausible model, an arbitrary quantile function will be a good first approximation. Finally, the inference procedures for quantile models need less information and are more robust to outliers.

TABLE OF CONTENTS
Preface.- Chapter I Quantile Functions.- Chapter II Quantile-Based Reliability Concepts.- Chapter III Quantile Function Models.- Chapter IV Ageing Concepts.- Chapter V Total Time on Test Transforms (TTT).- Chapter VI L-Moments of Residual Life and Partial Moments.- Chapter VII Nonmonotone Hazard Quantile Functions.- Chapter VIII Stochastic Orders in Reliability.- IX Estimation and Modeling.- References.- Index.