Distribution-Free Inference for Welfare Indices under Complete and Incomplete Information

Statistical Inference for Welfare under Complete and Incomplete Information

We show how a collection of results in the literature on the empirical estimation of welfare indicators from sample data can be unified. We also demonstrate how some of these ideas can be extended to empirically important cases where the data have been trimmed or censored.

Distribution of Mutual Information from Complete and Incomplete Data

Mutual information is widely used, in a descriptive way, to measure the stochastic dependence of categorical random variables. In order to address questions such as the reliability of the descriptive value, one must consider sample-to-population inferential approaches. This paper deals with the posterior distribution of mutual information, as obtained in a Bayesian framework by a second-order D...

Model Checking Abilities under Incomplete Information Is Indeed Delta2-complete

We study the model checking complexity of Alternating-time temporal logic with imperfect information and imperfect recall (atlir). Contrary to what we have stated in [11], the problem turns out to be∆2 -complete, thus con rming the initial intuition of Schobbens [18]. We prove ∆2 -hardness through a reduction of the SNSAT problem, while the membership in∆ P 2 stems from the algorithm presented ...

Bargaining under Incomplete Information

Stable Mixing of Complete and Incomplete Information

An increasing number of parameter estimation tasks involve the use of at least two information sources, one complete but limited, the other abundant but incomplete. Standard algorithms such as EM (or em) used in this context are unfortunately not stable in the sense that they can lead to a dramatic loss of accuracy with the inclusion of incomplete observations. We provide a more controlled solu...