“No boots on the ground”: the effectiveness of US drones against Al Qaeda in the Arabian Peninsula

The number of US drone strikes against Al Qaeda in the Arabian Peninsula (AQAP) in Yemen has increased significantly since 2010, but received limited academic attention. This article examines the effectiveness of this drone campaign using an existing theoretical framework. Crucially, we contribute to the framework by adding a fourth intervening variable, namely target correspondence. Through a single case study, it is found that drones have only enjoyed limited success in degrading AQAP's hierarchical structure, qualified human resources and access to key material resources, and in some cases – owing to our target correspondence analysis – this limited success has come at a price. It is found that drones have temporarily disrupted AQAP by successfully eliminating senior leaders involved in coordinating and overseeing external operations. Overall, AQAP's ability to hit Western targets remains significant.     

The effect of nonnormality on variables sampling plans

Acceptance sampling plans based on variables have been in use for many years. Recently, there has been a renewal of interest in these plans, because of the relative efficiencies that they offer with respect to attributes sampling regarding sample size. Furthermore, in situations where acceptable quality levels are very small, and a high level of protection is desired, variables sampling is often much more efficient than attributes sampling. An important disadvantage of variables sampling is that the distribution of the parameter being inspected must be known. Most standard variables sampling plans assume that the distribution of this parameter is normal. This article examines the effect of the normality assumption in variables sampling. Methods to detect departures from normality are reviewed.     

Aggregating assignment constraints

This article shows how simple systems of linear equations with {0,1} variables can be aggregated into a single linear equation whose {0,1} solutions are identical to the solutions of the original system. Structures of the original systems are exploited to keep the aggregator's integer coefficients from becoming unnecessarily large. The results have potential application in integer programming and information theory, especially for problems that contain assignment-type constraints along with other constraints. Several unresolved questions of a number-theoretic nature are mentioned at the conclusion of the article.     

An analysis of constrained robust regression estimators

Multicollinearity and nonnormal errors are problems often encountered in the application of linear regression. Estimators are proposed for dealing with the simultaneous occurrence of both multicollinearity and nonnormality. These estimators are developed by combining biased estimation techniques with certain robust criteria. An iteratively reweighted least-squares procedure is used to compute the estimates. The performance of the combined estimators is studied empirically through Monte Carlo experiments structured according to factorial designs. With respect to a mean-squared-error criterion, the combined estimators are superior to ordinary least-squares, pure biased estimators, and pure robust estimators when multicollinearity and nonnormality are present. The loss in efficiency for the combined estimators relative to least squares is small when these problems do not occur. Some guidelines for the use of these combined estimators are given.