Presenter: Dr Mikis Stasinopoulos, London Metropolitan University
Date: Monday 22 September
Venue: Lecture theatre E6A 102, Macquarie University campus, North Ryde
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).
Enquiries
Gillian Heller, email: gheller@efs.mq.edu.au, phone (02) 9850 8541
More Information
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., 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, 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, 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.
WHO Multicentre Growth Reference Study Group (2007) WHO Child Growth Standards: Head circumference-for-age, arm circumference-for-age, triceps circumference-for-age and subscapular skinford-for-age: Methods and development. <http://www.who.int/childgrowth/en> Geneva: World Health Organization.
