Information capacity designs for generalized linear models

Swan, Taryn and McGree, James and Lewis, Susan and Woods, Dave (2010) Information capacity designs for generalized linear models. In: Royal Statistical Society 2010 International Conference, 13-17 Sept 2010, Brighton, UK.

Abstract

Information Capacity (IC) is a criterion for selecting a design for an experiment based on its effectiveness in estimating a set of models to be investigated in the data analysis (Li and Nachtsheim (2000)). This presentation will describe how this criterion can be applied to experiments where a generalized linear model (GLM) describes the measured response. Three different types of IC designs will be compared with the aim of achieving accurate estimation of the models in the set and discriminating between the competing models. Recent work on experiments for both estimation and discrimination in nonlinear models, includes Waterhouse et al. (2009).


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Poster)
Refereed: No
Item Status: Live Archive
Additional Information: Poster presentation.
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering
Date Deposited: 16 Jul 2018 02:17
Last Modified: 02 Aug 2018 05:33
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences
URI: http://eprints.usq.edu.au/id/eprint/34527

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