000			01875nam a2200289Ia 4500
003			OSt
005			20251222155951.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9780817683603
037			_cTextbook
040			_aCSL _beng _cCSL
041			_aeng
084			_aB281, Q3;1 TB _qCSL
100			_aNair, N. Unnikrishanan. _eauthor. _9858761
245		0	_aQuantile-based Reliability Analysis
260			_aNew York: _bBrikhauser, _c2013.
300			_axx, 397p. _b: ill.
500			_aReferences 361-384p.; Index 385-390p.; Author Index 391-397p.
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.
650			_a Nanomonotone _9858762
650			_a Stochastic orders _9858763
650			_aTest transforms _9858764
700			_aSankaran, P. G. _eco-author. _9858765
700			_aBalakrishnan, N. _eco-author. _9858766
942			_hB281, Q3;1 TB _cTB _2CC _n0
999			_c13705 _d13705