THE TALMUD RULE AND THE SECUREMENT OF AGENTS? AWARDS

This paper provides a new characterization of the Talmud rule by means of a new property, called securement. This property says that any agent holding a feasible claim will get at least one nht of her claim, where n is the number of agents involved. We show that securement together with a weak version of path independence and the standard properties of self-duality and consistency characterize the Talmud rule.bankruptcy problems, Talmud rule, characterization results

Weakest Collective Rationality and the Nash Bargaining Solution

We propose a new axiom, Weakest Collective Rationality (WCR) which is weaker than both Weak Pareto Optimality (WPO) in Nash (1950)’s original characterization and Strong Individual Rationality (SIR) in Roth (1977)’s characterization of the Nash bargaining solution. We then characterize the Nash solution by Symmetry (SYM), Scale Invariance (SI), Independence of Irrelevant Alternatives (IIA) and our Weakest Collective Rationality (WCR) axiom.Nash Bargaining Solution, Pareto Optimality, Strong Individual Rationality, Weak Pareto Optimality, Weakest Collective Rationality

On posets and independence spaces

AbstractBy constructing a correspondence relationship between independence spaces and posets, under isomorphism, this paper characterizes loopless independence spaces and applies this characterization to reformulate certain results on independence spaces in poset frameworks. These state that the idea provided in this paper is a new approach for the study of independence spaces. We outline our future work finally

Two-dimensional Kolmogorov-type Goodness-of-fit Tests Based on Characterizations and their Asymptotic Efficiencies

In this paper new two-dimensional goodness of fit tests are proposed. They are of supremum-type and are based on different types of characterizations. For the first time a characterization based on independence of two statistics is used for goodness-of-fit testing. The asymptotics of the statistics is studied and Bahadur efficiencies of the tests against some close alternatives are calculated. In the process a theorem on large deviations of Kolmogorov-type statistics has been extended to the multidimensional case

Kernel dimension reduction in regression

We present a new methodology for sufficient dimension reduction (SDR). Our methodology derives directly from the formulation of SDR in terms of the conditional independence of the covariate $X$ from the response $Y$, given the projection of $X$ on the central subspace [cf. J. Amer. Statist. Assoc. 86 (1991) 316--342 and Regression Graphics (1998) Wiley]. We show that this conditional independence assertion can be characterized in terms of conditional covariance operators on reproducing kernel Hilbert spaces and we show how this characterization leads to an $M$-estimator for the central subspace. The resulting estimator is shown to be consistent under weak conditions; in particular, we do not have to impose linearity or ellipticity conditions of the kinds that are generally invoked for SDR methods. We also present empirical results showing that the new methodology is competitive in practice.Comment: Published in at http://dx.doi.org/10.1214/08-AOS637 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org