04291nam a22005295i 4500 978-0-8176-8361-0 DE-He213 20260521092034.0 cr nn 008mamaa 130823s2013 xxu| s |||| 0|eng d 9780817683610 99780817683610 10.1007/978-0-8176-8361-0 doi 519.5 23 Nair, N. Unnikrishnan. author. Quantile-Based Reliability Analysis [electronic resource] / by N. Unnikrishnan Nair, P.G. Sankaran, N. Balakrishnan. New York, NY : Springer New York : Imprint: Birkhäuser, 2013. XX, 397 p. 20 illus., 3 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Statistics for Industry and Technology 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. 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.   Quantile-Based Reliability Analysis's innovative methodology is laid out in a well-organized sequence of topics, including:   ·       Definitions and properties of reliability concepts in terms of quantile functions; ·       Ageing concepts and their interrelationships; ·       Total time on test transforms; ·       L-moments of residual life; ·       Score and tail exponent functions and relevant applications; ·       Modeling problems and stochastic orders connecting quantile-based reliability functions.   An ideal text for advanced undergraduate and graduate courses in reliability and statistics, Quantile-Based Reliability Analysis also contains many unique topics for study and research in survival analysis, engineering, economics, and the medical sciences. In addition, its illuminating discussion of the general theory of quantile functions is germane to many contexts involving statistical analysis.   STATISTICS. DISTRIBUTION (PROBABILITY THEORY). MATHEMATICAL STATISTICS. ECONOMICS STATISTICS. STATISTICS. STATISTICAL THEORY AND METHODS. PROBABILITY THEORY AND STOCHASTIC PROCESSES. STATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY AND EARTH SCIENCES. STATISTICS FOR BUSINESS/ECONOMICS/MATHEMATICAL FINANCE/INSURANCE. STATISTICS FOR LIFE SCIENCES, MEDICINE, HEALTH SCIENCES. MATHEMATICAL MODELING AND INDUSTRIAL MATHEMATICS. Sankaran, P.G. author. Balakrishnan, N. author. SpringerLink (Online service) Springer eBooks Printed edition: 9780817683603 Statistics for Industry and Technology http://dx.doi.org/10.1007/978-0-8176-8361-0 Ver el texto completo en las instalaciones del CICY ZDB-2-SMA ddc ER 35830 35830 0 0 ddc 0 0 LE CICY CICY EL 2025-10-06 0 519.5 2025-10-06 08:44:41 2025-10-06 ER