Kernel monotonicity and concavity plus tail-conditional mean inequalities characterize multiple stochastic orders in parametric density families, extending to compound sums and nonmonotone cases.
Sufficient conditions for some stochastic orders of discrete random variables with applications in reliability
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Weaker shape conditions on likelihood ratios, including unimodality, limited sign changes with negative tail, or superlevel-set criteria, suffice for endpoint criteria in hazard-rate and usual stochastic orders.
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Kernel Characterisations of Stochastic Orders Within Parametric Density Families
Kernel monotonicity and concavity plus tail-conditional mean inequalities characterize multiple stochastic orders in parametric density families, extending to compound sums and nonmonotone cases.
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Stochastic Ordering under Weaker Likelihood-Ratio Shape Conditions
Weaker shape conditions on likelihood ratios, including unimodality, limited sign changes with negative tail, or superlevel-set criteria, suffice for endpoint criteria in hazard-rate and usual stochastic orders.