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SUMMARY:A flexible regression approach  using GAMLSS - Mikis Stasinopoulos
 \, London Metropolitan University
DTSTART:20100420T133000Z
DTEND:20100420T143000Z
UID:TALK23434@talks.cam.ac.uk
CONTACT:Michael Sweeting
DESCRIPTION:Generalized Additive Models for Location\, Scale and Shape (GA
 MLSS) were introduced by Rigby and Stasinopoulos (2005). They refer to a v
 ery general regression type model in which both the systematic and random 
 parts of the model are highly \nflexible and where the fitting algorithm i
 s fast enough to allow the rapid exploration of very large and complex dat
 a sets. GAMLSS is a general framework for univariate regression type stati
 stical problems. In GAMLSS the exponential family distribution assumption 
 used in Generalized Linear Model (GLM) and Generalized Additive Model (GAM
 )\, (see Nelder and Wedderburn\, 1972 and Hastie and Tibshirani\,\n1990\, 
 respectively) is relaxed and replaced by a very general distribution famil
 y including highly skew and kurtotic discrete and continuous distributions
 . The systematic part of the model is expanded to allow modelling not only
  the mean (or location) but all the other parameters of the distribution o
 f y as linear parametric\, non-linear parametric and/or additive\n(smoothi
 ng) non-parametric functions of explanatory variables and/or random effect
 s terms. Maximum (penalized) likelihood estimation is used to\nfit the mod
 els. For medium to large size data\, GAMLSS allow \nflexibility\n in stati
 stical modelling far beyond other currently available methods. The GAMLSS 
 framework is implemented in R.\n\nThe most important application of GAMLSS
  up to now is its use by the Department of Nutrition for Health and Develo
 pment of the World Health Organization to construct the worldwide standard
  growth centile curves\,\nsee WHO(2006). The range of possible application
 s for GAMLSS is very wide and examples will be given of its usefulness in 
 modelling data.\nIn the talk we will describe the GAMLSS model\, the varie
 ty of different (two\, three and four parameter) distributions that are im
 plemented within\nthe R GAMLSS package and the variety of different additi
 ve (smoothing) terms that can be used. New distributions and new additive 
 terms can be\nadded easily to the R package. The use of censored data\, tr
 uncate distributions and finite mixture of distributions within the GAMLSS
  framework\, will also be described.\n\n*References*\n\n\nHastie\, T.J.\, 
 and Tibshirani\, R.J. (1990) Generalized Additive Models. London: Chapman 
 & Hall.\n\n\nIhaka\, R.\, and Gentleman\, R. (1996)\, A Language for Data 
 Analysis and Graphics\, Journal of Computational and Graphical Statistics\
 , 5\,3\,299-314.\n\n\nNelder\, J.A. and Wedderburn\, R.W.M. (1972) General
 ized Linear Models. J. R. Statist. Soc. A\, 135\, 370-384.\n\n\nRigby\, R.
 A. and Stasinopoulos\, D.M. (2005) Generalized Additive Models for Locatio
 n\, Scale and Shape (with discussion). Appl. Statist.\, 54\, 1-38.\n\n\nWH
 O Multicentre Growth Reference Study Group (2006) WHO Child Growth Standar
 ds: Length/height-for-age\, weight-for-age\, weight-for-length\, weight-fo
 r-height and body mass index-for-age: Methods and development. Geneva: Wor
 ld Health Organization.
LOCATION:Large Seminar Room\, 1st Floor\, Institute of Public Health\, Uni
 versity Forvie Site\, Robinson Way\, Cambridge
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