Search results for "Stochastic ordering"

showing 6 items of 6 documents

Fractional generalized cumulative entropy and its dynamic version

2021

Following the theory of information measures based on the cumulative distribution function, we propose the fractional generalized cumulative entropy, and its dynamic version. These entropies are particularly suitable to deal with distributions satisfying the proportional reversed hazard model. We study the connection with fractional integrals, and some bounds and comparisons based on stochastic orderings, that allow to show that the proposed measure is actually a variability measure. The investigation also involves various notions of reliability theory, since the considered dynamic measure is a suitable extension of the mean inactivity time. We also introduce the empirical generalized fract…

FOS: Computer and information sciencesExponential distributionComputer Science - Information TheoryMathematics - Statistics TheoryStatistics Theory (math.ST)01 natural sciencesMeasure (mathematics)010305 fluids & plasmas0103 physical sciencesFOS: MathematicsApplied mathematicsAlmost surelyCumulative entropy; Fractional calculus; Stochastic orderings; EstimationEntropy (energy dispersal)010306 general physicsStochastic orderingsMathematicsCentral limit theoremNumerical AnalysisInformation Theory (cs.IT)Applied MathematicsCumulative distribution functionProbability (math.PR)Fractional calculusEmpirical measureFractional calculusModeling and SimulationEstimationCumulative entropyMathematics - ProbabilityCommunications in Nonlinear Science and Numerical Simulation
researchProduct

On the fractional probabilistic Taylor's and mean value theorems

2016

In order to develop certain fractional probabilistic analogues of Taylor's theorem and mean value theorem, we introduce the nth-order fractional equilibrium distribution in terms of the Weyl fractional integral and investigate its main properties. Specifically, we show a characterization result by which the nth-order fractional equilibrium distribution is identical to the starting distribution if and only if it is exponential. The nth-order fractional equilibrium density is then used to prove a fractional probabilistic Taylor's theorem based on derivatives of Riemann-Liouville type. A fractional analogue of the probabilistic mean value theorem is thus developed for pairs of nonnegative rand…

Generalized Taylor’s formulaMean value theoremSurvival bounded order01 natural sciencesStochastic ordering010104 statistics & probabilityCharacterization of exponential distribution; Fractional calculus; Fractional equilibrium distribution; Generalized Taylor’s formula; Mean value theorem; Survival bounded orderFOS: MathematicsCharacterization of exponential distributionApplied mathematics0101 mathematicsMathematicsComputer Science::Information RetrievalApplied MathematicsProbability (math.PR)010102 general mathematicsProbabilistic logic60E99 26A33 26A24Fractional calculusFractional equilibrium distributionFractional calculusExponential functionDistribution (mathematics)Bounded functionMean value theorem (divided differences)Random variableAnalysisMathematics - Probability
researchProduct

Conditional convex orders and measurable martingale couplings

2014

Strassen's classical martingale coupling theorem states that two real-valued random variables are ordered in the convex (resp.\ increasing convex) stochastic order if and only if they admit a martingale (resp.\ submartingale) coupling. By analyzing topological properties of spaces of probability measures equipped with a Wasserstein metric and applying a measurable selection theorem, we prove a conditional version of this result for real-valued random variables conditioned on a random element taking values in a general measurable space. We also provide an analogue of the conditional martingale coupling theorem in the language of probability kernels and illustrate how this result can be appli…

Statistics and Probability01 natural sciencesStochastic ordering010104 statistics & probabilitysymbols.namesakeMathematics::ProbabilityStrassen algorithmWasserstein metricmartingale couplingvektorit (matematiikka)FOS: MathematicsApplied mathematics0101 mathematicsstokastiset prosessitMathematicsProbability measurekytkentäconvex stochastic ordermatematiikka010102 general mathematicsProbability (math.PR)Random elementMarkov chain Monte Carloconditional couplingincreasing convex stochastic orderpointwise couplingsymbols60E15probability kernelMartingale (probability theory)Random variableMathematics - Probability
researchProduct

A Unified Approach to Likelihood Inference on Stochastic Orderings in a Nonparametric Context

1998

Abstract For data in a two-way contingency table with ordered margins, we consider various hypotheses of stochastic orders among the conditional distributions considered by rows and show that each is equivalent to requiring that an invertible transformation of the vectors of conditional row probabilities satisfies an appropriate set of linear inequalities. This leads to the construction of a general algorithm for maximum likelihood estimation under multinomial sampling and provides a simple framework for deriving the asymptotic distribution of log-likelihood ratio tests. The usual stochastic ordering and the so called uniform and likelihood ratio orderings are considered as special cases. I…

Statistics and ProbabilityCombinatoricsIndependent and identically distributed random variablesLinear inequalityTransformation (function)Likelihood-ratio testAsymptotic distributionApplied mathematicsConditional probability distributionStatistics Probability and UncertaintyStochastic orderingStatistical hypothesis testingMathematicsJournal of the American Statistical Association
researchProduct

Stochastic order characterization of uniform integrability and tightness

2013

We show that a family of random variables is uniformly integrable if and only if it is stochastically bounded in the increasing convex order by an integrable random variable. This result is complemented by proving analogous statements for the strong stochastic order and for power-integrable dominating random variables. Especially, we show that whenever a family of random variables is stochastically bounded by a p-integrable random variable for some p>1, there is no distinction between the strong order and the increasing convex order. These results also yield new characterizations of relative compactness in Wasserstein and Prohorov metrics.

Statistics and ProbabilityDiscrete mathematicsPure mathematicsRandom fieldMultivariate random variableProbability (math.PR)ta111Random functionRandom element60E15 60B10 60F25Stochastic orderingFunctional Analysis (math.FA)Mathematics - Functional AnalysisRandom variateConvergence of random variablesStochastic simulationFOS: MathematicsStatistics Probability and UncertaintyMathematics - ProbabilityMathematicsStatistics & Probability Letters
researchProduct

Stochastic ordering of classical discrete distributions

2010

For several pairs $(P,Q)$ of classical distributions on $\N_0$, we show that their stochastic ordering $P\leq_{st} Q$ can be characterized by their extreme tail ordering equivalent to $ P(\{k_\ast \})/Q(\{k_\ast\}) \le 1 \le \lim_{k\to k^\ast} P(\{k\})/Q(\{k\})$, with $k_\ast$ and $k^\ast$ denoting the minimum and the supremum of the support of $P+Q$, and with the limit to be read as $P(\{k^\ast\})/Q(\{k^\ast\})$ for $k^\ast$ finite. This includes in particular all pairs where $P$ and $Q$ are both binomial ($b_{n_1,p_1} \leq_{st} b_{n_2,p_2}$ if and only if $n_1\le n_2$ and $(1-p_1)^{n_1}\ge(1-p_2)^{n_2}$, or $p_1=0$), both negative binomial ($b^-_{r_1,p_1}\leq_{st} b^-_{r_2,p_2}$ if and on…

Statistics and ProbabilityWaiting timeApplied MathematicsProbability (math.PR)010102 general mathematicsCoupling (probability)Poisson distribution01 natural sciencesStochastic orderingInfimum and supremumHypergeometric distributionCombinatorics010104 statistics & probabilitysymbols.namesakeFOS: MathematicsMonotone likelihood ratiosymbolsLimit (mathematics)60E150101 mathematicsMathematics - ProbabilityMathematicsAdvances in Applied Probability
researchProduct