A robust regression technique using compound estimation

Least squares fitting of regression models is a widely used technique. The presence of outliers in the data can have an adverse effect on the method of least squares, resulting in a model that does not adequately fit to the bulk of the data. For this situation, robust regression techniques have been proposed as an improvement to the method of least squares. We propose a robust regression procedure that performs well relative to the current robust methods against a variety of dataset types. Evaluations are performed using datasets without outliers (testing efficiency), with a large percentage of outliers (testing breakdown), and with high leverage outliers (testing bounded influence). The datasets are based on 2-level factorial designs that include axial points to evaluate leverage effects. A Monte Carlo simulation approach is used to evaluate the estimating capability of the proposed procedure relative to several competing methods. We also provide an application to estimating costs for government satellites. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 125–139, 1998     

Cost-based due-date assignment with the use of classical and neural-network approaches

Traditional methods of due-date assignment presented in the literature and used in practice generally assume cost-of-earliness and cost-of-tardiness functions that may bear little resemblance to true costs. For example, practitioners using ordinary least-squares (OLS) regression implicitly minimize a quadratic cost function symmetric about the due date, thereby assigning equal second-order costs to early completion and tardy behavior. In this article the consequences of such assumptions are pointed out, and a cost-based assignment scheme is suggested whereby the cost of early completion may differ in form and/or degree from the cost of tardiness. Two classical approaches (OLS regression and mathematical programming) as well as a neural-network methodology for solving this problem are developed and compared on three hypothetical shops using simulation techniques. It is found for the cases considered that: (a) implicitly ignoring cost-based assignments can be very costly; (b) simpler regression-based rules cited in the literature are very poor cost performers; (c) if the earliness and tardiness cost functions are both linear, linear programming and neural networks are the methodologies of choice; and (d) if the form of the earliness cost function differs from that of the tardiness cost function, neural networks are statistically superior performers. Finally, it is noted that neural networks can be used for a wide range of cost functions, whereas the other methodologies are significantly more restricted. © 1997 John Wiley & Sons, Inc.     

On the orbit characteristics of the M/G/ 1 retrial queue

In teletraffic applications of retrial queues only the service zone is observable. Another part of a retrial queue, the orbit, which represents the delay before repeated attempts to get service, cannot be observed. Thus, it is very important to get general results about behavior of the orbit. We investigate two characteristics of the orbit, namely, the orbit busy period and the orbit idle period, which seem to be very useful from this point of view. © 1996 John Wiley & Sons, Inc.     

Operating Costs of Aging Air Force Aircraft: Adjusting for Aggregate Budget Effects

The rate at which operating costs grow as aircraft age is important for setting operating budgets and for deciding when to replace aging systems. While studies using data from the 1990's typically found 1–3 percent real rates of growth in operating costs as aircraft age, studies using data from the 2000's found greater rates, for example in the 4–6 percent range. Growth in the total U.S. Air Force budget during the 2000s appears to explain much of the higher estimated annual growth rates in operating costs per flying hour beyond the growth rate intrinsic to the aging of the fleet.     

Repression Substitution: Shifting Human Rights Violations in Response to UN Naming and Shaming

Does the United Nations naming and shaming of specific violations of human rights decrease government repression? In this article, we argue that international shaming of specific human rights violations can weaken the target government, bringing new challenges and making the government cessation of repression less feasible. When international naming and shaming campaigns target specific repressive tactics, they increase the costs of some – but not all – means of repression. Using original data on naming and shaming by the United Nations Human Rights Council (HRC), we show that the shaming of one physical integrity violation is jointly associated with decreases in that violation and increases in other violations of human rights.     

Can Torture Be Justified?

Torture requires careful definition, because of the degree to which its definition often entails its moral condemnation. Torture involves the deliberate infliction of (intense) pain for coercive or punishment reasons. While emphasizing that the act of torture is indeed naturally seen as repulsive and ideally should not take place, I offer a non-utilitarian argument to ethically justify torture in specific kinds of interrogative cases. This argument closely examines the moral isomorphism between cases of immediate and delayed self-defense, showing that in both cases lethal force is justified. I further show that, once one’s life is forfeit, one’s other rights pertaining to the defense of one’s intended victims are likewise forfeit. As such, any form of interrogative torture necessary to procuring relevant information from persons involved in a lethal attack upon innocent persons is ethically justified. However, that an action is strictly ethically justified does not entail that it ought to be generally adopted. Several potent constitutional and sociological concerns are raised and considered.     

A stochastic air combat logistics decision model for Blue versus Red opposition

Technologically advanced aircraft rely on robust and responsive logistics systems to ensure a high state of operational readiness. This paper fills a critical gap in the literature for combat models by closely relating effectiveness of the logistics system to determinants of success in combat. We present a stochastic diffusion model of an aerial battle between Blue and Red forces. The number of aircraft of Blue forces aloft and ready to be aloft on combat missions is limited by the maximum number of assigned aircraft, the reliability of aircraft subsystems, and the logistic system's ability to repair and replenish those subsystems. Our parsimonious model can illustrate important trade‐offs between logistics decision variables and operational success.     

Shortest path interdiction problem with arc improvement recourse: A multiobjective approach

We consider the shortest path interdiction problem involving two agents, a leader and a follower, playing a Stackelberg game. The leader seeks to maximize the follower's minimum costs by interdicting certain arcs, thus increasing the travel time of those arcs. The follower may improve the network after the interdiction by lowering the costs of some arcs, subject to a cardinality budget restriction on arc improvements. The leader and the follower are both aware of all problem data, with the exception that the leader is unaware of the follower's improvement budget. The effectiveness of an interdiction action is given by the length of a shortest path after arc costs are adjusted by both the interdiction and improvement. We propose a multiobjective optimization model for this problem, with each objective corresponding to a different possible improvement budget value. We provide mathematical optimization techniques to generate a complete set of strategies that are Pareto‐optimal. Additionally, for the special case of series‐parallel graphs, we provide a dynamic‐programming algorithm for generating all Pareto‐optimal solutions.     

The optimal linear combination of control variates in the presence of asymptotically negligible bias

The optimal linear combination of control variates is well known when the controls are assumed to be unbiased. We derive here the optimal linear combination of controls in the situation where asymptotically negligible bias is present. The small-sample linear control which minimizes the mean square error (MSE) is derived. When the optimal asymptotic linear control is used rather than the optimal small-sample control, the degradation in MSE is c/n³, where n is the sample size and c is a known constant. This analysis is particulary relevant to the small-sample theory for control variates as applied to the steady-state estimation problem. Results for the method of multiple estimates are also given.     

Economic impact of the ministry of defence’s budget: methodological design and results for the Spanish economy

This article analyses the economic impact of the expenditure budget of the Spanish Ministry of Defence (MoD) and its Autonomous Agencies (AA), distinguishing direct, indirect and induced effects. The input–output methodology is used to find intersectoral effects on the rest of the economy. The article quantifies the economic impact in terms of production, gross value added (GVA), employed population, tax revenue, and also in terms of its contribution to the gross domestic product (GDP) of Spain in 2010. The results show that the activity of the MoD and AA generates 1.2% of the country’s GDP and 1.7% of total employment in that year.