Book review

New Zealand and the European Union edited by Matthew Gibbons. Auckland, Pearson Education. 2008. 172 p. NZ$52.99 (paperback). ISBN 0733993831.

Going bush. New Zealanders and nature in the twentieth century by Kirstie Ross. Auckland, Auckland University Press. 2008. 200 p. NZ$34.99 (paperback). ISBN 1869404246.

Globalisation and the wealth of nations by Brian Easton. Auckland, Auckland University Press. 2007. 248 p. NZ$50.00 (paperback). ISBN 9781869403775.     

Combined estimate of a location parameter

In this paper, we revisit the problem of combining estimates of location considered by Cohen (1976). Our results unify and strengthen the results of Cohen (1976), Bhattacharya (1981) and Akai (1982).     

Non-linear models 1

This is the first application of a new method for testing stationary random point processes. Consider the class of all stationary ergodic point processes on the real line with arbitrary dependences among the inter–point distances (spacing).The hypothesis is :The observed process φ is a homogeneous Poisson process or more (resp.less) regular than a Poisson process.The sample is the vector of the first n points t₁, …,t_n.There is a close relation between our method for testing and queueing theory: For finding an appropriate test statistic, we observe the behaviour of a single server queue with the input φ.A table of critical values is given.     

Managing green productivity: Some strategic directions

The twentieth century has been the hallmark for managing total productivity, where the emphasis is towards maximization of outputs commensurate with the consumption of inputs. This is being challenged now to evolve the world class manufacturing strategy requiring a paradigm shift. This shift is towards green productivity, which looks forward to socially appropriate production and consumption aiming at value innovation and resource conservation. This paper is an attempt to outline the concepts and experiential learning about green productivity.     

A clustering approach to interpretable principal components

A new method for constructing interpretable principal components is proposed. The method first clusters the variables, and then interpretable (sparse) components are constructed from the correlation matrices of the clustered variables. For the first step of the method, a new weighted-variances method for clustering variables is proposed. It reflects the nature of the problem that the interpretable components should maximize the explained variance and thus provide sparse dimension reduction. An important feature of the new clustering procedure is that the optimal number of clusters (and components) can be determined in a non-subjective manner. The new method is illustrated using well-known simulated and real data sets. It clearly outperforms many existing methods for sparse principal component analysis in terms of both explained variance and sparseness.     

Variance bounds for estimators in autoregressive models with constraints

We consider nonlinear and heteroscedastic autoregressive models whose residuals are martingale increments with conditional distributions that fulfil certain constraints. We treat two classes of constraints: residuals depending on the past through some function of the past observations only, and residuals that are invariant under some finite group of transformations. We determine the efficient influence function for estimators of the autoregressive parameter in such models, calculate variance bounds, discuss information gains, and suggest how to construct efficient estimators. Without constraints, efficient estimators can be given by weighted least squares estimators. With the constraints considered here, efficient estimators are obtained differently, as one-step improvements of some initial estimator, similarly as in autoregressive models with independent increments.     

Matrix mean square error comparisons based on a certain covariance structure

For two given estimators of a parameter vector the covariance structure of their difference is used to compare them in terms of their mean square error matrices. The results obtained are applied to the covariance adjustment technique and regression     

Book reviews and books received

Abstract

In the field of stress research it is often assumed that social support as well as coping behaviour contributes to feelings of health and well-being. However, until now it has not been altogether clear whether a main-effect model or a buffer model was the more appropriate for explaining the role of these factors in response to stressful situations. In this paper, a research project is described in which both the direct and indirect effects of social support and coping were studied in the work situation of nurses. The results offer more support for a main-effect model than for a buffer or moderating-effect model, especially with regard to the coping strategies ‘active problem-solving’ and ‘palliative behaviour’. However, some moderating effects on nurses‘ reactions to their work situation were found for social support and for ‘comforting cognitions’ as a coping strategy.     

Influence diagnostics in a multivariate normal regression model with general parameterization

Patriota and Lemonte [24] introduced a quite general multivariate normal regression model. This model considers that the mean vector and the covariance matrix share the same vector of parameters. In this paper we present some influence assessment for this model, such as the local influence, total local influence of an individual and generalized leverage which are discussed. Additionally, the normal curvatures for local influence studies are derived under some perturbation schemes.