04153nam a22004815i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003500137082001400172100003600186245012300222264007100345300006300416336002600479337002600505338003600531347002400567490004300591505043100634520182801065650001602893650003902909650002902948650002702977650001603004650003603020650004903056650008903105650007003194650006103264650005403325700002803379700003003407710003403437773002003471776003603491830004303527856010103570978-0-8176-8361-0DE-He21320260521092034.0cr nn 008mamaa130823s2013    xxu|    s    |||| 0|eng d  a9780817683610  a997808176836107 a10.1007/978-0-8176-8361-02doi04a519.52231 aNair, N. Unnikrishnan.eauthor.10aQuantile-Based Reliability Analysish[electronic resource] /cby N. Unnikrishnan Nair, P.G. Sankaran, N. Balakrishnan. 1aNew York, NY :bSpringer New York :bImprint: Birkhäuser,c2013.  aXX, 397 p. 20 illus., 3 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aStatistics for Industry and Technology0 aPreface -- 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.  aQuantile-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.   0aSTATISTICS. 0aDISTRIBUTION (PROBABILITY THEORY). 0aMATHEMATICAL STATISTICS. 0aECONOMICSxSTATISTICS.14aSTATISTICS.24aSTATISTICAL THEORY AND METHODS.24aPROBABILITY THEORY AND STOCHASTIC PROCESSES.24aSTATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY AND EARTH SCIENCES.24aSTATISTICS FOR BUSINESS/ECONOMICS/MATHEMATICAL FINANCE/INSURANCE.24aSTATISTICS FOR LIFE SCIENCES, MEDICINE, HEALTH SCIENCES.24aMATHEMATICAL MODELING AND INDUSTRIAL MATHEMATICS.1 aSankaran, P.G.eauthor.1 aBalakrishnan, N.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780817683603 0aStatistics for Industry and Technology40uhttp://dx.doi.org/10.1007/978-0-8176-8361-0zVer el texto completo en las instalaciones del CICY