Responding to September 11: A Conflict Resolution Scholar/Practitioner's Perspective

We find ourselves thrust into a deadly conflict of potentially very large scale. This conflict may proceed in several different directions, some of which might prove quite destructive, involving high numbers of further civilian casualties on all sides. There are lessons to be drawn from both theory and practice in the field of conflict resolution that may be helpful in considering how to proceed. In this paper, we review a few important concepts from the field of conflict resolution, attempt to point out where some of these dynamics are occurring in the current situation, and highlight some potential pitfalls. Specific policy recommendations based on this analysis are offered, and the question of how we can best ensure that our response to the attacks does not lead to a degenerative cycle of violence that further destabilizes the Middle East and undermines American long-term interests is addressed.     

Simultaneous estimation of several CDF’s: homogeneity constraint

Let {x_ij(1 ? j ? n_i)|i = 1, 2, …, k} be k independent samples of size n_j from respective distributions of functions F_j(x)(1 ? j ? k). A classical statistical problem is to test whether these k samples came from a common distribution function, F(x) whose form may or may not be known. In this paper, we consider the complementary problem of estimating the distribution functions suspected to be homogeneous in order to improve the basic estimator known as “empirical distribution function” (edf), in an asymptotic setup. Accordingly, we consider four additional estimators, namely, the restricted estimator (RE), the preliminary test estimator (PTE), the shrinkage estimator (SE), and the positive rule shrinkage estimator (PRSE) and study their characteristic properties based on the mean squared error (MSE) and relative risk efficiency (RRE) with tables and graphs. We observed that for k ? 4, the positive rule SE performs uniformly better than both shrinkage and the unrestricted estimator, while PTEs works reasonably well for k < 4.