Processes,procedures and journal development: Past,present and future

This paper is written as an overview of developments relating to the Journal. The authors explain the background to their editorship and the plans and structures they implemented. They provide a progress report and also discuss the process of running the Journal so as to explain to potential authors how the systems operate and the philosophy of the Journal.     

A GRAPHICAL DIAGNOSTIC FOR VARIANCE FUNCTIONS

This paper proposes diagnostic plots for regression variance functions. It shows how to extend graphical methodology that uses Bayesian sampling for checking the regression mean function to also check the variance function. Plots can be constructed quickly and easily for any model of interest. These plots help to identify model weaknesses and can suggest ways to make improvements. The proposed methodology is illustrated with two examples: a simple linear regression model to fix ideas, and a more complex study involving count data to demonstrate the potential for wide application.     

Wavelet Threshold Estimators for Data with Correlated Noise

Wavelet threshold estimators for data with stationary correlated noise are constructed by applying a level-dependent soft threshold to the coefficients in the wavelet transform. A variety of threshold choices is proposed, including one based on an unbiased estimate of mean-squared error. The practical performance of the method is demonstrated on examples, including data from a neurophysiological context. The theoretical properties of the estimators are investigated by comparing them with an ideal but unattainable `bench-mark', that can be considered in the wavelet context as the risk obtained by ideal spatial adaptivity, and more generally is obtained by the use of an `oracle' that provides information that is not actually available in the data. It is shown that the level-dependent threshold estimator performs well relative to the bench-mark risk, and that its minimax behaviour cannot be improved on in order of magnitude by any other estimator. The wavelet domain structure of both short- and long-range dependent noise is considered, and in both cases it is shown that the estimators have near optimal behaviour simultaneously in a wide range of function classes, adapting automatically to the regularity properties of the underlying model. The proofs of the main results are obtained by considering a more general multivariate normal decision theoretic problem.     

Managing as a Performing Art

This paper considers the activities of senior managers as isomorphic with the activities of actors. It takes performing as not a matter of metaphor, but a matter of form; life at the top of an organization is intrinsically theatrical; each of us is blessed or cursed with histrionic sensibility. Proceeding by way of a comparison of Edmund Kean and Lee lacocca it touches upon matters of text and interpretation, rehearsal and performance and the importance of individuation. The argument – such as it is – is that both Kean and lacocca perform themselves, the former's Richard III, the latter's Chrysler being the fullest realizations of that which was, hitherto, inchoate and emergent. The final part of the paper is concerned with the implications of this perspective for education, training and development; current management education appears geared to reduce rather than to promote individuality. Techniques are imposed and answers are provided and the entire educational performance revolves around teachers as performers rather than managers as performers. The way to become a management star, it is suggested, is to do managing, not simply to be audience for academic stars.     

Relations between statistics for testing exponentiality and uniformity

It is shown that Greenwood's statistic for uniformity and the Hahn-Shapiro and Stephens statistics for exponentiality with known origin are equivalent. It is also shown that the distribution of the Shapiro-Wilk statistic for testing the hypothesis of exponentiality with unknown origin is obtainable from the distribution of Greenwood's statistic.