NDLI: Generalized Increasing Convex and Directionally Convex Orders

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Generalized Increasing Convex and Directionally Convex Orders

Content Provider	Scilit
Author	Denuit, Michel M. Mesfioui, Mhamed
Copyright Year	2010
Description	In this paper, the componentwise increasing convex order, the upper orthant order, the upper orthant convex order, and the increasing directionally convex order for random vectors are generalized to hierarchical classes of integral stochastic order relations. The elements of the generating classes of functions possess nonnegative partial derivatives up to some given degrees. Some properties of these new stochastic order relations are studied. Particular attention is paid to the comparison of weighted sums of the respective components of ordered random vectors. By providing a unified derivation of standard multivariate stochastic orderings, the present paper shows how some well-known results derive from a common principle.
Related Links	https://www.cambridge.org/core/services/aop-cambridge-core/content/view/A0D99A405CBD9CAF550612B8AF8A9636/S0021900200006537a.pdf/div-class-title-generalized-increasing-convex-and-directionally-convex-orders-div.pdf http://projecteuclid.org/download/pdfview_1/euclid.jap/1269610830
Ending Page	276
Page Count	13
Starting Page	264
ISSN	00219002
e-ISSN	14756072
DOI	10.1239/jap/1269610830
Journal	Journal of applied probability
Issue Number	1
Volume Number	47
Language	English
Publisher	Cambridge University Press (CUP)
Publisher Date	2010-03-01
Access Restriction	Open
Subject Keyword	Journal of applied probability Artificial Intelligence Convex Order Stochastic Order Increasing Convex Convex and Directionally
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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