Managing business portfolios in German companies

This study analyzes the behavior of top managers in managing their firms' business portfolios. A review of the existing literature provides a conceptual framework for a research design combining quantitative and qualitative techniques. Data were collected through interviews with 35 top managers of well-known German business firms and the findings of this study translated into guidelines for managers. The results suggest that successful top managers should be concerned with the social relationships and management processes within the corporate office, as well as with their use of management concepts in managing the business portfolio.     

A comparison between two simple measures of skewness

In 1995, Arnold and Groeneveld introduced the measure of skewness gammaM in terms of F(mode)-the cumulative probability of a random variable less than or equal to the mode of the distribution. They assumed that the mode of a distribution exists and is unique. Independently, in 1996, the present author arrived at the measure of skewness T, which is given in terms of F(mean). This measure possesses desirable properties and is equally simple. The measure gammaM satisfies - 1 gammaM 1 , with 1 (- 1) indicating extreme right (left) skewness. However, the measure T can take on any value on the real line; hence, an equivalent measure gammaT is considered and is compared with gammaM. We consider a variety of families of distributions and include in our study other measures of skewness of interest. Skewness values are easily obtained using MINITAB programs.     

Multilevel models for repeated binary outcomes: attitudes and voting over the electoral cycle

Models for fitting longitudinal binary responses are explored by using a panel study of voting intentions. A standard multilevel repeated measures logistic model is shown to be inadequate owing to a substantial proportion of respondents who maintain a constant response over time. A multivariate binary response model is shown to be a better fit to the data.     

Robust F-tests for linear models

The author presents a robust F-test for comparing nested linear models. It is suggested that the approach will be attractive to practitioners because it is based on the familiar F-statistic and corresponds to the common practice of reporting F-statistics after removing obvious outliers. It is calibrated in terms of a real parameter that can be directly interpreted as the willingness of the data analyst to remove observations, and the sensitivity of the F-statistic to this parameter is easily examined. The procedure is evaluated with a simulation study where a scale mixture distribution is used to generate outliers. The procedure is also applied to some data where the occurrence of an outlier is confounded with the significance of a regression term. This provides a comparison of two competing models for the data: one removing an outlier and the other including an additional regression term instead.     

Population policies in advanced societies: Pronatalist and migration strategies

This paper discusses pronatalist and migration policies that have been introduced in selected European countries. Measures and objectives are briefly described before an evaluation of the efficacy of each is provided. The case studies chosen show that pronatalist policies have had limited effects. Migration policies seem to have been more effective, although their aims so far have been largely non-demographic. Migration strategies will increasingly have to be considered for demographic reasons in the future, to counterbalance population decline. Both pronatalist and migration strategies tend to conflict with other policies (social and economic policies, emancipation or housing policies), and to be in competition with the demands of an ageing society. Limits to the development or expansion of either strategy are spelled out. Finally a few untried mechanisms are mentioned.     

Prior distribution assessment for a multivariate normal distribution: an experimental study

A variety of methods of eliciting a prior distribution for a multivariate normal (MVN) distribution have recently been proposed. This paper reports an experiment in which 16 meteorologists used the methods to quantify their opinions about climatology variables. Our results compare prior models and show, in particular, that it can be better to assume the mean and variance of an MVN distribution are independent a priori, rather than to model opinion by the conjugate prior distribution. Using a proper scoring rule, different forms of assessment task are examined and alternative ways of estimating parameters are compared. To quantify opinion about means, it proved preferable to ask directly about the means rather than individual observations while, to quantify opinion about the variance matrix, it was best to ask about deviations from the mean. Further results include recommendations for the way parameters of the prior distribution are estimated.     

On Non-parametric Estimation of the Survival Function with Competing Risks

The non-parametric maximum likelihood estimators (MLEs) are derived for survival functions associated with individual risks or system components in a reliability framework. Lifetimes are observed for systems that contain one or more of those components. Analogous to a competing risks model, the system is assumed to fail upon the first instance of any component failure; i.e. the system is configured in series. For any given risk or component type, the asymptotic distribution is shown to depend explicitly on the unknown survival function of the other risks, as well as the censoring distribution. Survival functions with increasing failure rate are investigated as a special case. The order restricted MLE is shown to be consistent under mild assumptions of the underlying component lifetime distributions.     

Distance sampling methodology

We consider the method of distance sampling described by Buckland, Anderson, Burnham and Laake in 1993. We explore the properties of the methodology in simple cases chosen to allow direct and accessible comparisons of distance sampling in the design- and model-based frameworks. In particular, we obtain expressions for the bias and variance of the distance sampling estimator of object density and for the expected value of the recommended analytic variance estimator within each framework. These results enable us to clarify aspects of the performance of the methodology which may be of interest to users and potential users of distance sampling.