000 04293nam a22005415i 4500
001 978-0-8176-8361-0
003 DE-He213
005 20140220082758.0
007 cr nn 008mamaa
008 130823s2013 xxu| s |||| 0|eng d
020 _a9780817683610
_9978-0-8176-8361-0
024 7 _a10.1007/978-0-8176-8361-0
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aNair, N. Unnikrishnan.
_eauthor.
245 1 0 _aQuantile-Based Reliability Analysis
_h[electronic resource] /
_cby N. Unnikrishnan Nair, P.G. Sankaran, N. Balakrishnan.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Birkhäuser,
_c2013.
300 _aXX, 397 p. 20 illus., 3 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStatistics for Industry and Technology
505 0 _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.
520 _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.  
650 0 _aStatistics.
650 0 _aDistribution (Probability theory).
650 0 _aMathematical statistics.
650 0 _aEconomics
_xStatistics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
650 2 4 _aMathematical Modeling and Industrial Mathematics.
700 1 _aSankaran, P.G.
_eauthor.
700 1 _aBalakrishnan, N.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780817683603
830 0 _aStatistics for Industry and Technology
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-8176-8361-0
912 _aZDB-2-SMA
999 _c94209
_d94209