Quantile-based Reliability Analysis (Record no. 13705)

MARC details
000 -LEADER
fixed length control field 01875nam a2200289Ia 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251222155951.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220909b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780817683603
037 ## - SOURCE OF ACQUISITION
Terms of availability Textbook
040 ## - CATALOGING SOURCE
Original cataloging agency CSL
Language of cataloging eng
Transcribing agency CSL
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
084 ## - COLON CLASSIFICATION NUMBER
Classification number B281, Q3;1 TB
Assigning agency CSL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Nair, N. Unnikrishanan.
Relator term author.
9 (RLIN) 858761
245 #0 - TITLE STATEMENT
Title Quantile-based Reliability Analysis
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New York:
Name of publisher, distributor, etc. Brikhauser,
Date of publication, distribution, etc. 2013.
300 ## - PHYSICAL DESCRIPTION
Extent xx, 397p.
Other physical details : ill.
500 ## - GENERAL NOTE
General note References 361-384p.; Index 385-390p.; Author Index 391-397p.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Nanomonotone
9 (RLIN) 858762
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Stochastic orders
9 (RLIN) 858763
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Test transforms
9 (RLIN) 858764
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Sankaran, P. G.
Relator term co-author.
9 (RLIN) 858765
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Balakrishnan, N.
Relator term co-author.
9 (RLIN) 858766
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Classification part B281, Q3;1 TB
Koha item type Textbook
Source of classification or shelving scheme Colon Classification (CC)
Suppress in OPAC No
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
        Central Science Library Central Science Library 2022-09-12 2956, 23/03/2015, Ashutosh Technical Books   B281 Q3;1 TB SL1598367 2022-09-12 2022-09-12 Textbook
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