Top-down proteomics has enabled the elucidation of heterogeneous protein complexes with different cofactors, post-translational modifications, and membership. This heterogeneity is believed to play a previously unknown role in cellular processes. The molecular forms complex have come be called “complex isoform” or “complexoform”. Despite complexoform, it remains unclear how whether circuits con...

In this paper we compare ruin functions for two risk processes with respect to stochastic ordering, stop-loss ordering and ordering of adjustment coeecients. The risk processes are as follows: in the Markov-modulated environment and the associated averaged compound Poisson model. In the latter case the arrival rate is obtained by averaging over time the arrival rate in the Markov modulated mode...

Introduction. We study a class of capacitated stochastic lot-sizing inventory control problems with stochastic, non-stationary and correlated demands that evolve over time. Economies of scale and capacity constraints exist in many practical scenarios. However, models with fixed ordering costs and capacity constraints are typically computationally intractable, and even the structure of the optim...

Use of comparison theorems for solutions of stochastic differential equations is made to prove sufficient conditions for almost sure or stochastic ordering of first passage times of one-dimensional diffusion processes through an assigned boundary. Conditions on boundary and parameters that make orded the first passage time distributions of two different diffusion processes are determined. Some ...

This paper presents a new stochastic tool, called convex ordering, that provides an ordering of any convex function of transmission rates of two protocols and valuable insights into high order behaviors of protocols. As the ordering determined by this tool is consistent with any convex function of rates, it can be applied to any unknown metric for protocol performance that consists of some high...

This paper compares n-dimensional random vectors in terms of their interdependence. We adopt the stochastic dominance approach, relating orderings of interdependence expressed directly in terms of joint probability distributions to orderings expressed indirectly through properties of objective functions whose expectations are used to evaluate distributions. Since the expected values of additive...

This article considers Bayesian inference on collections of unknown distributions subject to a partial stochastic ordering. To address problems in testing of equalities between groups and estimation of group-specific distributions, we propose classes of restricted dependent Dirichlet process (rDDP) priors. These rDDP priors have full support in the space of stochastically ordered distributions,...

For several pairs (P,Q) of classical distributions on N0, we show that their stochastic ordering P ≤st Q can be characterized by their extreme tail ordering equivalent to P ({k∗})/Q({k∗}) ≤ 1 ≤ limk→k∗ P ({k})/Q({k}), with k∗ and k∗ denoting the minimum and the supremum of the support of P + Q, and with the limit to be read as P ({k∗})/Q({k∗}) for k∗ finite. This includes in particular all pair...