Terrorism: A Peace Psychological Analysis

Peace psychologists emphasize the importance of recognizing both direct and structural bases of peace and conflict. In the peace psychological analysis of terrorism presented here, I attempt to further our understanding of the bases of terrorism in the context of how inequities in political, economic, and social structures may feed into or exacerbate terrorism. I analyze the effectiveness of responses to terrorism in terms of the tripartite peacekeeping, peacemaking, and peacebuilding model of nonviolent response to violence.     

Consistency of Procrustes Estimators

Lele has shown that the Procrustes estimator of form is inconsistent and raised the question about the consistency of the Procrustes estimator of shape. In this paper the consistency of estimators of form and shape is studied under various assumptions. In particular, it is shown that the Procrustes estimator of shape is consistent under the assumption of an isotropic error distribution and that consistency breaks down if the assumption of isotropy is relaxed. The relevance of these results for practical shape analysis is discussed. As a by-product, some new results are derived for the offset uniform distribution from directional data.     

Multivariate spatial interpolation and exposure to air pollutants

We develop and apply an approach to the spatial interpolation of a vector-valued random response field. The Bayesian approach we adopt enables uncertainty about the underlying models to be représentés in expressing the accuracy of the resulting interpolants. The methodology is particularly relevant in environmetrics, where vector-valued responses are only observed at designated sites at successive time points. The theory allows space-time modelling at the second level of the hierarchical prior model so that uncertainty about the model parameters has been fully expressed at the first level. In this way, we avoid unduly optimistic estimates of inferential accuracy. Moreover, the prior model can be upgraded with any available new data, while past data can be used in a systematic way to fit model parameters. The theory is based on the multivariate normal and related joint distributions. Our hierarchical prior models lead to posterior distributions which are robust with respect to the choice of the prior (hyperparameters). We illustrate our theory with an example involving monitoring stations in southern Ontario, where monthly average levels of ozone, sulphate, and nitrate are available and between-station response triplets are interpolated. In this example we use a recently developed method for interpolating spatial correlation fields.     

Bahadur efficiency and local optimality of a test for the exponential distribution based on the gini statistic

Summary The sample scale-free Gini index is known to be a powerful test of exponentiality against a broad class of alternatives. To understand better the efficiency properties of this test we calculate its Bahadur efficiency for most commonly used parametric alternatives to the exponential distribution. Using variational arguments and the Bahadur-Raghavachari inequality for exact slopes we find the conditions of local Bahadur optimality of the Gini test. It turns out that this property surprisingly holds for a family of alternative distributions including the well-known Gompertz-Makeham distribution. Partially supported by Russian Fund of Fundamental Research, grants No. 95-01-1260 and 96-01-0852.     

Resource allocation and project selection: Control of r & d under dynamic process of data improvement

This paper deals with the problem of optimal control of R&D. The fundamental aspects of this problem are viewed as: uncertainty about economic parameters of new technology (or new activity, e.g. a search for new oil fields) explored by a group of parallel projects attempting to make advances in this technology as well as improve economic data; allocation of investments among rival projects under a given total budget; timing of the ultimate selection of new technology (activity) according to the most successful project. The process of data improvement (PDI) is described as a (random) process of reducing the intervals of uncertainty, the rate of data improvement being controlled, in effect, by the investment policy. A stationary dynamic model with infinite horizon of planning and with the criterion of total discounted costs is developed. The model is carefully investigated from the economic point of view, and some new effects are discovered. It is demonstrated that uncertainty may be the source of additional gain; the bigger the initial uncertainty, the bigger the gain may be under optimal control. This effect is based on PDI, the economic background of which is presented as multiplication of the expected gain over time, which is typical for the classical macromodels of economics.Optimal continuous control is constructed at every time moment, and answers to two main questions are given:(1) either to prolong R & D or to make ultimate selection of one project, (2) how to allocate resources among the rival projects in the case of R&D prolongation. The last question is answered on the basis of profitability indices that relate efficiencies of PDI for particular projects to the costs of information.     

ARE ESTIMATES OF SEX DISCRIMINATION BY EMPLOYERS ROBUST? THE USE OF NEVER-MARRIEDS

Current decomposition estimates of sex discrimination by employers are not robust. Many "unobservables," like motivation and attitudes toward work, are left unmeasured. We estimate sex discrimination with two plausible methods of controlling for a major unobservable, acceptance of male and female traditional roles in the household. The methods offer enormously different estimates of sex discrimination. One estimates sex discrimination at over 61 percent of the female wage, the other finds little sex discrimination and possibly favoritism toward women. The range in estimates is so large that point estimates of sex discrimination by employers are of little use to policymakers.