Minimum phi-divergence estimator and hierarchical testing in loglinear models

In this paper we consider inference based on very general divergence measures, under assumptions of multinomial sampling and loglinear models. We define the minimum phi-divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. This estimator is then used in a phi-divergence goodness-of-fit statistic, which is the basis of two new statistics for solving the problem of testing a nested sequence of loglinear models

Get Rid of Unanimity Rule: The Superiority of Majority Rules with Veto Power

We study unanimous decision making under incomplete information. We argue that all unanimous decision rules are not equivalent. We show that majority rules with veto power are (i) Pareto superior to commonly used unanimous rules, and (ii) ex-ante efficient in a broad class of situations