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Bivariate models from univariate life distributions: A characterization cum modeling approach
Author(s) -
Roy Dilip
Publication year - 2004
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/nav.20027
Subject(s) - bivariate analysis , univariate , class (philosophy) , characterization (materials science) , mathematics , convolution (computer science) , econometrics , marginal distribution , interdependence , joint probability distribution , statistics , computer science , multivariate statistics , random variable , artificial intelligence , sociology , social science , materials science , artificial neural network , nanotechnology
Bivariate life distribution models are of importance for studying interdependent components. We present a generic approach by introducing a new concept of characterized model in stead of a characterized distribution. It strikes a balance between characterization and modeling approaches to eliminate their individual limitations and incorporate their respective strengths. The proposed model, being a characterized one, admits many important properties irrespective of the choice of marginal distributions. The retention of univariate IFR, DFR, IFRA, DFRA, NBU, and NWU class properties in the bivariate setup has been ensured along with some results on series combinations and convolution. No other models, available in the literature, can ensure simultaneous retention of these fundamental and extremely important class properties. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004