DETERMINING THE APPROPRIATE DISCOUNT RATES IN PURE CAPITAL RATIONING

This paper proposes an interpretation of the pure capital rationing problem as it is faced by many managers in decentralized firms in which budgets serve as the principal means of control. It is argued that the appropriate objective for situations such as these is the maximization of either undiscounted earnings over the planning horizon or total value of the investments at the horizon. When either objective function is used in conjunction with the frequently encountered linear programming models for the capital rationing problem, shadow prices result which give rise to discount rates that will reproduce the optimal solution using discounted cash flow as a criterion. These results are then used as a means for clarifying several confusing and misleading statements that have appeared in the literature.     

Japanese and American Children's Reasoning about Accepting Credit for Prosocial Behavior

Children's reasoning about the appropriateness of accepting credit for one's own prosocial behavior was examined. Participants aged 7–11 years old in Japan and the USA (total N = 206) were presented with a series of stories in which a protagonist performs a good deed and is asked about it by another character. Across stories, the protagonist either truthfully acknowledges the deed or falsely denies it, in a statement that is made either in public or in private, and is addressed to either a teacher or to a peer. As predicted, Japanese children judged protagonists less favorably when they acknowledged the good deed in public rather than in private. Further, Japanese children tended to view modest lies more favorably overall than did children in the USA. These results point to the importance of modesty in Japan and to the ways in which Japanese children take into account the social context of communication when deciding whether it is appropriate for individuals to convey information about themselves.     

Effects of a Punitive Environment on Children's Executive Functioning: A Natural Experiment

Few studies have examined the influence of environmental factors on children's executive functioning (EF) performance. The present study examined the effects of a punitive vs. non‐punitive school environment on West African children's EF skills. Tasks included a ‘cool’ (relatively non‐affective) and ‘hot’ (relatively affective/motivational) version of three EF tasks: delay of gratification; gift delay; and dimensional change card sort. Children had more difficulties with the hot versions of the tasks than the cool versions, and older children outperformed younger children. After controlling for verbal ability (Peabody picture vocabulary test‐third edition), a consistent pattern of interaction between school and grade level emerged. Overall, kindergarten children in the punitive school performed no differently than their counterparts in the non‐punitive school. However, in grade 1, children in the punitive school performed significantly worse than their counterparts in the non‐punitive school. These results point to the need to consider interactions among discipline style, age, and internalization processes of self‐regulation to better understand environmental influences on EF development.     

A hierarchical model for extreme wind speeds

Summary.  A typical extreme value analysis is often carried out on the basis of simplistic inferential procedures, though the data being analysed may be structurally complex. Here we develop a hierarchical model for hourly gust maximum wind speed data, which attempts to identify site and seasonal effects for the marginal densities of hourly maxima, as well as for the serial dependence at each location. A Gaussian model for the random effects exploits the meteorological structure in the data, enabling increased precision for inferences at individual sites and in individual seasons. The Bayesian framework that is adopted is also exploited to obtain predictive return level estimates at each site, which incorporate uncertainty due to model estimation, as well as the randomness that is inherent in the processes that are involved.     

Bayesian analysis of a general growth curve model with predictions using power transformations and AR(1) autoregressive dependence

In this paper, we consider a Bayesian analysis of the unbalanced (general) growth curve model with AR(1) autoregressive dependence, while applying the Box-Cox power transformations. We propose exact, simple and Markov chain Monte Carlo approximate parameter estimation and prediction of future values. Numerical results are illustrated with real and simulated data.     

Importance of interpolation when constructing double-bootstrap confidence intervals

We show that, in the context of double-bootstrap confidence intervals, linear interpolation at the second level of the double bootstrap can reduce the simulation error component of coverage error by an order of magnitude. Intervals that are indistinguishable in terms of coverage error with theoretical, infinite simulation, double-bootstrap confidence intervals may be obtained at substantially less computational expense than by using the standard Monte Carlo approximation method. The intervals retain the simplicity of uniform bootstrap sampling and require no special analysis or computational techniques. Interpolation at the first level of the double bootstrap is shown to have a relatively minor effect on the simulation error.     

Mathematical morphology: A useful set of tools for image analysis

In this paper we give an overview of both classical and more modern morphological techniques. We will demonstrate their utility through a range of practical examples. After discussing the fundamental morphological ideas, we show how the classic morphological opening and closing filters lead to measures of size via granulometries, and we will discuss briefly their implementation. We also present an overview of morphological segmentation techniques, and the use of connected openings and thinnings will be demonstrated. This then leads us into the more recent set-theoretic notions of graph based approaches to image analysis.     

Two ways of modelling overdispersion in non-normal data

For non-normal data assumed to have distributions, such as the Poisson distribution, which have an a priori dispersion parameter, there are two ways of modelling overdispersion: by a quasi-likelihood approach or with a random-effect model. The two approaches yield different variance functions for the response, which may be distinguishable if adequate data are available. The epilepsy data of Thall and Vail and the fabric data of Bissell are used to exemplify the ideas.     

A case–control study relating railroad worker mortality to diesel exhaust exposure using a threshold regression model

A case–control study of lung cancer mortality in U.S. railroad workers in jobs with and without diesel exhaust exposure is reanalyzed using a new threshold regression methodology. The study included 1256 workers who died of lung cancer and 2385 controls who died primarily of circulatory system diseases. Diesel exhaust exposure was assessed using railroad job history from the US Railroad Retirement Board and an industrial hygiene survey. Smoking habits were available from next-of-kin and potential asbestos exposure was assessed by job history review. The new analysis reassesses lung cancer mortality and examines circulatory system disease mortality. Jobs with regular exposure to diesel exhaust had a survival pattern characterized by an initial delay in mortality, followed by a rapid deterioration of health prior to death. The pattern is seen in subjects dying of lung cancer, circulatory system diseases, and other causes. The unique pattern is illustrated using a new type of Kaplan–Meier survival plot in which the time scale represents a measure of disease progression rather than calendar time. The disease progression scale accounts for a healthy-worker effect when describing the effects of cumulative exposures on mortality.