Research in statistical education — planning of an international survey

As announced in the November 2000 issue of MathStats&OR [1], one of the projects supported by the Maths, Stats & OR Network funds is an international survey of research into pedagogic issues in statistics and OR. I am taking the lead on this and report here on the progress that has been made during the first year. A paper giving some background to the project and describing initial thinking on how it might be implemented was presented at the 53rd session of the International Statistical Institute in Seoul, Korea, in August 2001 in a session on The future of statistics education research [2]. It sounded easy. I considered that I was something of an expert on surveys having lectured on the topic for many years and having helped students and others who were doing surveys, particularly with the design of their questionnaires. Surely all I had to do was to draft a few questions, send them electronically to colleagues in statistical education who would be only to happy to respond, and summarise their responses? I should have learnt from my experience of advising all those students who thought that doing a survey was easy and to whom I had to explain that their ideas were too ambitious. There are several inter-related stages in survey research and it is important to think about these before rushing into the collection of data. In the case of the survey in question, this planning stage revealed several challenges. Surveys are usually done for a purpose so even before planning how to do them, it is advisable to think about the final product and the dissemination of results. This is the route I followed.

Statistical analysis of extreme forest fires

Forest fires can cause extensive damage to natural resources and properties. They can also destroy wildlife habitat, affect the forest ecosystem and threaten human lives. In this paper extreme wildland fires are analysed using a point process model for extremes. The model based on a generalised Pareto distribution is used to model data on acres of wildland burnt by extreme fire in the US since 1825. A semi-parametric smoothing approach is adapted with maximum likelihood method to estimate model parameters.

Statistical investigations - drawing it all together

The author's approach to teaching an integrative unit to a small group of master’s level Applied Statistics students in 2000-2001 is described. Details of the various activities such as data analysis, reading and discussion of papers, and training in consultancy skills are given, as also are details of the assessment. The students’ and lecturer’s views of the unit are discussed.

A new parameterization of Johnson's SB distribution with application to fitting forest tree diameter data

The SB distributional model of Johnson's 1949 paper was introduced by a transformation to normality, that is, z ~ N(0, 1), consisting of a linear scaling to the range (0, 1), a logit transformation, and an affine transformation, z = γ + δu. The model, in its original parameterization, has often been used in forest diameter distribution modelling. In this paper, we define the SB distribution in terms of the inverse transformation from normality, including an initial linear scaling transformation, u = γ′ + δ′z (δ′ = 1/δ and γ′ = �γ/δ). The SB model in terms of the new parameterization is derived, and maximum likelihood estimation schema are presented for both model parameterizations. The statistical properties of the two alternative parameterizations are compared empirically on 20 data sets of diameter distributions of Changbai larch (Larix olgensis Henry). The new parameterization is shown to be statistically better than Johnson's original parameterization for the data sets considered here.

The role of space charge formation in the generation of thermally stimulated current (TSC) spectroscopy data for a model amorphous drug system

Thermally stimulated current (TSC) spectroscopy is attracting increasing attention as a means of materials characterization, particularly in terms of measuring slow relaxation processes in solid samples. However, wider use of the technique within the pharmaceutical field has been inhibited by difficulties associated with the interpretation of TSC data, particularly in terms of deconvoluting dipolar relaxation processes from charge distribution phenomena. Here, we present evidence that space charge and electrode contact effects may play a significant role in the generation of peaks that have thus far proved difficult to interpret. We also introduce the use of a stabilization temperature in order to control the space charge magnitude. We have studied amorphous indometacin as a model drug compound and have varied the measurement parameters (stabilization and polarization temperatures), interpreting the changes in spectral composition in terms of charge redistribution processes. More specifically, we suggested that charge drift and diffusion processes, charge injection from the electrodes and high activation energy charge redistribution processes may all contribute to the appearance of shoulders and 'spurious' peaks. We present recommendations for eliminating or reducing these effects that may allow more confident interpretation of TSC data.