An Access Grid Mathematical Sciences colloquium
(part of the Colloquium Series of the School of Mathematical Sciences, Monash University)

Speaker: Prof Zbigniew Michalewicz, University of Adelaide

Time: 12:00 noon (SA time), Building Q1-01, Mawson Lakes Campus.

The talk addresses a gap in the educational curriculum for 1st year students by proposing a new course that aims at getting students to think about how to frame and solve unstructured problems. The idea is to increase the student’s mathematical awareness and problem-solving skills by discussing a variety of puzzles. The talk makes an argument that this approach – called Puzzle-Based Learning – is very beneficial for introducing mathematics, critical thinking, and problem-solving skills.

The new course has been approved by the University of Adelaide for Faculty of Engineering, Computer Science, and Mathematics. Many other universities are in the process of introducing such a course. The course will be offered in two versions:

• (a) full-semester course and
• (b) a unit within general course (e.g. Introduction to Engineering).

All teaching materials (power point slides, assignments, etc.) are being prepared. The new textbook (Puzzle-Based Learning: Introduction to Critical Thinking, Mathematics, and Problem Solving) is available from June 2008. The talk provides additional information on this development.

Seminar Convenor: Pamila Phillips (Pamila.Phillips@unisa.edu.au).
AGR Tech Support: Richard Rawinski (ichard.rawinski@unisa.edu.au).

If you (and your colleagues) wish to participate, please:

1. book your own AGR (or university/APAC etc. AGR that you otherwise are able to use), and ask your AGR technical people to contact Richard (the AGR technical person at uniSA); and

2. inform Pamila (the seminar convenor) of your intention to participate.

XXII International Congress of Theoretical and Applied Mechanics

The Congress, ICTAM 2008, was invited by the Australian Academy of Science upon the recommendation of the Australian and New Zealand Theoretical and Applied Mechanics communities. A consortium of universities in South Australia will host the meeting, at the Adelaide Convention Centre.

9th Australasian Conference on Mathematics and Computers in Sport

Location:

Twin Towns Resort, Tweed Heads, NSW

Director:

Dr. John Hammond (jhammond@scu.edu.au)

Accommodation bookings:

Twin Towns Resort www.twintowns.com.au

The Department of Statistics at Macquarie University is hosting a one-day short course on Generalized Additive Models for Location, Scale and Shape (GAMLSS).

• Presenter: Dr Mikis Stasinopoulos, London Metropolitan University.

• Enquiries: Gillian Heller, email: gheller@efs.mq.edu.au, phone: (02) 9850 8541.

Generalized Additive Models for Location, Scale and Shape (GAMLSS) are (semi-)parametric regression type models. They are parametric, in that they require a parametric distribution assumption for the response variable, and "semi" in the sense that the modelling of the parameters of the distribution, as functions of explanatory variables, may involve using non-parametric smoothing functions.

GAMLSS were introduced by Rigby and Stasinopoulos (2001, 2005) and Akantziliotou et al. (2002) as a way of overcoming some of the limitations associated with the popular Generalized Linear Models (GLM) and Generalized Additive Models (GAM), Nelder and Wedderburn (1972) and Hastie and Tibshirani (1990) respectively.

In GAMLSS the exponential family distribution assumption for the response variable, y, is relaxed and replaced by a general distribution family, including highly skew and/or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only the mean (or location) but all the parameters of the distribution of y as linear and/or nonlinear parametric and/or additive non-parametric functions of explanatory variables and/or random effects.

Hence GAMLSS is especially suited to modelling a response variable which does not follow an exponential family distribution, (eg. leptokurtic or platykurtic and/or positive or negative skew response data, or overdispersed counts) or which exhibit heterogeneity (e.g. where the scale or shape of the distribution of the response variable changes with explanatory variables(s)).

The GAMLSS framework of statistical modelling is implemented in a series of packages in R. The packages allow the user to fit more than 50 different distributions including the Box Cox Power Exponential distribution (Rigby and Stasinopoulos, 2004) used by the World Health Organization for the construction of the world standard growth curves, [WHO Multicentre Growth Reference Study Group (2006, 2007)]. It also allows the fitting of truncated, censored or finite mixture versions of the distributions. The short course will include two practical sessions.

Short courses on GAMLSS have previously been given by Drs. Stasinopoulos and Rigby at the Univeristy of Utrecht (2006), University of Palermo (2007), and the International Workshop on Statistical Modelling, Utrecht (2008).

References

• Akantziliotou, K. Rigby, R. A. and Stasinopoulos, D. M. (2002) The R implementation of Generalized Additive Models for Location, Scale and Shape in: Statistical modelling in Society: Proceedings of the 17th International Workshop on statistical modelling, ed: Stasinopoulos, M. and Touloumi, G., pp.75–83, Chania, Greece.

• Hastie, T. J. and Tibshirani, R. J. (1990), Generalized Additive Models, Chapman and Hall, London.

• Nelder, J. A. and Wedderburn, R. W. M., (1972) Generalized linear models, J. R. Statist. Soc. A., 135, pp.370–384.

• Rigby, R. A. and Stasinopoulos, D. M. (2001), The GAMLSS project: a flexible approach to statistical modelling, in: New Trends in Statistical Modelling: Proceedings of the 16th International Workshop on Statistical Modelling, ed: Klein, B. and Korsholm, L, pp.249–256, Odense, Denmark.

• Rigby, R. A. and Stasinopoulos D. M. (2004). Smooth centile curves for skew and kurtotic data modelled using the Box-Cox Power Exponential distribution http://studweb.north.londonmet.ac.uk/~stasinom/papers/boxcoxpower23.pdf, Statistics in Medicine, 23, pp.3053–3076.

• Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized Additive Models for Location, Scale and Shape, (with discussion). Appl. Statist., 54, pp.507–554.

• WHO Multicentre Growth Reference Study Group (2006) WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. http://www.who.int/childgrowth/en Geneva: World Health Organization.