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Dear All Re: Hierarchical Generalized Linear Models Workshop with Prof. R. W. Payne, VSNi, Hemel Hempstead, UK. 23 November 2006. There is only 1 place remaining on next week's HGLMs workshop. If you would like to join us please do contact me ASAP to make sure it has your name on it! :o) with best wishes Anna F Kane Training & Customer Support Manager VSN International Ltd. EM : [log in to unmask] ADR: 5 The Waterhouse, Waterhouse Street, Hemel Hempstead HP1 1ES (England) TEL: +44-(0)1442-450230 FAX: +44-(0)870-1215653 WEB: www.vsni.co.uk Register for all the latest VSN news at www.vsni.co.uk/resources/newsletter _____________________________________________________________________________ Hierarchical Generalized Linear Models (HGLMs) provide a flexible framework for modelling non-Normal data in situations when there may be several sources of error variation. They are defined by extending the familiar generalized linear models (GLMs) to include additional random terms in the linear predictor. They include generalized linear mixed models as a special case, but do not constrain the additional terms to follow a Normal distribution nor to have an identity link. The methodology also allows for the modelling of the dispersion of the error terms, and leads to an efficient fitting algorithm which does not involve numerical integration. This workshop, to take place at the VSNi training rooms in Hemel Hempstead, UK on 23rd November 2006, will introduce the underlying theory of HGLM's and discuss case studies illustrating where HGLMs can be useful. It will use the GenStat menu system and procedures developed by Payne, Lee, Nelder and Noh to implement the methodology and to accompany the forthcoming book on HGLMs by Lee, Nelder & Pawitan* (2006). Who should attend? Anyone with an interest in HGLMs or wishing to extend their advanced modelling knowledge. Prerequisite skills: 1. A good working knowledge of the GenStat system. 2. Experience of regression and generalized linear models. Costs: * £450 (excl. VAT). ________ * Lee, Y., Nelder, J.A. & Pawitan, Y. (2006). Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood. CRC Press.