Book reviews

Use of Force: The Practise of States by A. Mark Weisburd, Penn: Penn State Press, University Park, 1997, ISBN 0-271-01679-5 (hbk), $65.00/£58.00, ISBN 0-271-01680-9 (pbk), $25.00/£22.50

Late Breaking Foreign Policy: the News Media's Influence on Peace Operations by Warren P. Strobel, Washington DC: United States Insititute of Peace Press, 1997, ISBN 1-878379-68-2 (hbk), £23.25, ISBN 1-878379-67-4 (pbk)

The Reluctant Sheriff: The United States After the Cold War by Richard N. Haass, New York: Council on Foreign Relations, 1997, 148pp, index, ISBN 0-87609-201-6 (hbk), $24.95

The UN, Peace and Force edited by Michael Pugh, London: Frank Cass, 1997, ISBN 0-7146-4795-4 (hbk), £29.50, ISBN 0-7146-4320-3 (pbk), £15.00

Confidence-Building in South East Asia by Malcolm Chalmers, Oxford: Westview Press, 1996, ISBN 1-85143-116-0 (pbk), £19.95

Britain's Strategic Nuclear Deterrent: From Before the V-Bomber to Beyond Trident by Robert H. Paterson, London: Frank Cass, 1997, ISBN 0-7146470-3 (hbk), £35.00, ISBN 0-7146-4297-5 (pbk) £18.00

OSS in China: Prelude to Cold War by Maochun Yu, London: Yale University Press, 1997, ISBN 0 300 06698-8 (hbk), £25.00

Preventing Violent Conflicts: A Strategy for Preventive Diplomacy by Michael S. Lund, Washington DC: US Institute of Peace Press, 1996, ISBN 1-878379-52-6 (pbk), £11.75     

Testing goodness of fit to the increasing failure rate family

One branch of the reliability literature is concerned with devising statistical procedures with various nonparametric “restricted family” model assumptions because of the potential improved operating characteristics of such procedures over totally nonparametric ones. In the single-sample problem with unknown increasing failure rate (IFR) distribution F, (1) maximum-likelihood estimators of F have been calculated, (2) upper or lower tolerance limits for F have been determined, and (3) tests of the null hypothesis that F is exponential have been constructed. Barlow and Campo proposed graphical methods for assessing goodness of fit to the IFR model when the validity of this assumption is unknown. This article proposes several analytic tests of the IFR null hypothesis based on the maximum distance and area between the cumulative hazard function and its greatest convex minorant (GCM), and the maximum distance and area between the total time on test statistic and its GCM. A table of critical points is provided to implement a specific test having good overall power properties.     

Testing goodness of fit to the increasing failure rate family with censored data

One important thrust in the reliability literature is the development of statistical procedures under various “restricted family” model assumptions such as the increasing failure rate (IFR) and decreasing failure rate (DFR) distributions. However, relatively little work has been done on the problem of testing fit to such families as a null hypothesis. Barlow and Campo proposed graphical methods for assessing goodness of fit to the IFR model in single-sample problems. For the same problem with complete data, Tenga and Santner studied several analytic tests of the null hypothesis that the common underlying distribution is IFR versus the alternative that it is not IFR for complete data. This article considers the same problem for the case of four types of censored data: (i) Type I (time) censoring, (ii) Type I1 (order statistic) censoring, (iii) a hybrid of Type I and Type I1 censoring, and (iv) random censorship. The least favorable distributions of several intuitive test statistics are derived for each of the four types of censoring so that valid small-sample-size α tests can be constructed from them. Properties of these tests are investigated.     

The two-on-one stochastic duel

The one-on-one stochastic duel is extended to the general two-on-one duel for the first time. The state equations, win probabilities, mean value, and variance functions are derived. The case where one side has Erlang (2) firing times and the other is negative exponential is compared with the corresponding “Stochastic Lanchester” and Lanchester models to demonstrate their nonequivalence.