A use of second-order co-orientation model in international public relations

This paper is an extension of a line of research that questions the possibility of applying the co-orientation theory in researching the communication behavior between “general” publics of nations. Here we included three nations, Slovenia, Croatia and Serbia and questioned publics’ opinions on investment policies between mentioned nations. We extended the notion of co-orientation in international relations to include not only evaluations of two countries’ attitudes towards a third country, but also evaluations of opinions the two countries have between themselves (and to which the first is only an observer). We called it the second-order co-orientation. So far accuracy has been seen as the right objective for public relations using co-orientation. Our results question centrality of accuracy as the key performance indicator of effectiveness of public relations programs in the context of co-orientation model.     

Complement to a comparative analysis of heuristics for the <Emphasis Type="Italic">p</Emphasis>-median problem

A recent comparison of evolutionary, neural network, and scatter search heuristics for solving the p-median problem is completed by (i) gathering or obtaining exact optimal values in order to evaluate errors precisely, and (ii) including results obtained with several variants of a variable neighborhood search (VNS) heuristic. For a first, well-known, series of instances, the average errors of the evolutionary and neural network heuristics are over 10% and more than 1000 times larger than that of VNS. For a second series, this error is about 3% while the errors of the parallel VNS and of a hybrid heuristic are about 0.01% and that of parallel scatter search even smaller.     

A new family of lifetime models

We introduce a new family of distributions by adding a parameter to the Marshall–Olkin family of distributions. Some properties of the new family of distributions are derived. A particular case of the family, a three-parameter generalization of the exponential distribution, is given special attention. The shape properties, moments, distributions of the order statistics, entropies and estimation procedures are derived. An application to a real data set is discussed.     

A POLITICAL ECONOMY THEORY OF PARTIAL DECENTRALIZATION

We revisit the classic problem of tax competition in the context of federal nations, and derive a positive theory of partial decentralization. A capital poor median voter chooses to use redistributive capital taxes to provide public goods. The expectation of high capital taxes, however, results in a small capital stock which lowers returns to redistribution. The median voter therefore wants to commit to a lower level of capital taxes. She does so by setting a partial degree of decentralization in the Constitution. The equilibrium degree of decentralization balances the positive effect of tax competition on capital taxes with the loss in redistribution that results. We show that the degree of decentralization is nonmonotonic in inequality, increasing in the redistributive efficiency of public good provision, and decreasing in capital productivity. When public goods are heterogeneous in their capacity to transfer funds, all voters agree that goods with high redistributive capacity should be decentralized.     

On the convergence of two sequential Monte Carlo methods for maximum a posteriori sequence estimation and stochastic global optimization

This paper addresses the problem of maximum a posteriori (MAP) sequence estimation in general state-space models. We consider two algorithms based on the sequential Monte Carlo (SMC) methodology (also known as particle filtering). We prove that they produce approximations of the MAP estimator and that they converge almost surely. We also derive a lower bound for the number of particles that are needed to achieve a given approximation accuracy. In the last part of the paper, we investigate the application of particle filtering and MAP estimation to the global optimization of a class of (possibly non-convex and possibly non-differentiable) cost functions. In particular, we show how to convert the cost-minimization problem into one of MAP sequence estimation for a state-space model that is “matched” to the cost of interest. We provide examples that illustrate the application of the methodology as well as numerical results.     

Confidence intervals for the ratio of two variances

In this paper we consider confidence intervals for the ratio of two population variances. We propose a confidence interval for the ratio of two variances based on the t-statistic by deriving its Edgeworth expansion and considering Hall's and Johnson's transformations. Then, we consider the coverage accuracy of suggested intervals and intervals based on the F-statistic for some distributions.     

Runs in a Bivariate Sequence Over the First Coordinate

Let (X ₁, Y ₁), (X ₂, Y ₂),… be a sequence of independent and identically distributed (i.i.d.) pairs of random variables with two possible outcomes at each coordinate. Runs in the second coordinate are considered until the appearance of the first success in the first coordinate.