Parametric discounting

A parametric approach to the problem of selection from amongst alternative investment opportunities is considered in a discounted cash flow framework. The approach employs differential cash flows, an uncertain planning horizon and a nested approach to composing cash flow parameters. This promotes consistent treatment of alternative investments, clarifies the effects of uncertainty, avoids some of the defects of internal rate of return and payback period criteria and allows direct comparison of best and next best options as primal and dual versions of the same model. Net present value, internal rate of return, annual average cost or payback period assessments may be selected post analysis. A domestic wall insulation example is used for illustrative purposes. This example is related to the hydroelectric power development decision which gave rise to the approach.     

A test of Wheeler’s closed-offer arbitration system: An experimental study

This study investigates the impact of two alternative forms of arbitration — conventional arbitration and final-offer arbitration — and whether or not prearbitration bargaining information is available to the arbitrator on negotiation behavior and outcomes. Contrary to Wheeler’s prediction, participants anticipating closed-offer arbitration neither conceded more nor reached more settlements than did participants anticipating open-offer arbitration. Participants anticipating final-offer arbitration made a greater final concession than did participants anticipating conventional arbitration.     

NEAREST NEIGHBOUR ANALYSIS OF UNEQUALLY REPLICATED TRIALS

There has been much work on the use of neighbouring plots to control environmental variation in the analysis of agricultural field experiments. In particular, the Residual Maximum Likelihood Neighbour (REMLN) analysis of Gleeson&Cullis (1987) appears very promising. The application of the REMLN analysis to an unequally replicated field trial augmented with an additional variety planted every six plots in a grid system is here compared with a covariance (COV) analysis using the neighbouring grid or check plot values as the covariate. The results indicate that the REMLN analysis gives more accurate estimates of treatment contrasts than the COV analyses, but that the estimate of treatment means can be biased. The bias depends on the mean of the check plot. This bias can be removed by adjusting the estimates of the treatment means such that their average equals the average of the raw means rather than that of the raw data.     

Improving the accuracy of migration age detail in multiple-area population forecasts

Population projections are often required for many geographical areas, and must be prepared with maximal computer and minimal analytical effort. At the same time, realistic age detail forecasts require a flexible means of treating age-specific net migration. This report presents a migration projection technique compatible with these constraints. A simplified version of Pittenger's model is used, where future migration patterns are automatically assigned from characteristics of historical patterns. A comparative test of age pattern accuracy for 1970–1980 indicates that this technique is superior to the commonly used plus-minus adjustment to historical rates.     

Influential observations in principal component analysis:a case study

A number of results have been derived recently concerning the influence of individual observations in a principal component analysis. Some of these results, particularly those based on the correlation matrix, are applied to data consisting of seven anatomical measurements on students. The data have a correlation structure which is fairly typical of many found in allometry. This case study shows that theoretical influence functions often provide good estimates of the actual changes observed when individual observations are deleted from a principal component analysis. Different observations may be influential for different aspects of the principal component analysis (coefficients, variances and scores of principal components); these differences, and the distinction between outlying and influential observations are discussed in the context of the case study. A number of other complications, such as switching and rotation of principal components when an observation is deleted, are also illustrated.     

Dependent masking and system life data analysis: Bayesian inference for two-component systems

Data from field operations of a system is often used to estimate the reliability of components. Under ideal circumstances, this system field data contains the time to failure along with information on the exact component responsible for the system failure. However, in many cases, the exact component causing the failure of the system cannot be identified, and is considered to be masked. Previously developed models for estimation of component reliability from masked system life data have been based upon the assumption that masking occurs independently of the true cause of system failure. In this paper we develop a Bayesian methodology for estimating component reliabilities from masked system life data when the probability of masking is dependent upon the true cause of system failure. The Bayesian approach is illustrated for the case of a two-component system of exponentially distributed components.