Model choice for prediction in generalized linear models
- 1 January 1988
- journal article
- research article
- Published by Taylor & Francis in Statistics
- Vol. 19 (3) , 369-382
- https://doi.org/10.1080/02331888808802110
Abstract
In generalized linear models, a prediction criterion is defined similar to AKAIKE'S information criterion (AIC), see AKAIKE (1973), based on the expectation of the KULL- BACK-LEIBLER information measure ((cf. KULLBACK ( 1959 )) of an estimated pseudo den¬sity and the true density. This criterion chooses a pseudo prediction model which is determined by a subset of explanatory variables and NELDER'S link function (cf. NELDER and WEDDEBUEN ( 1972 )). The estiniatioii of this criterion leads to the definition of a statistic which has an analvtic expression in the gaussiao linear mooch as in the r>aper of BUNKE and DROGE ( 1984a, 1984b ) while in other cases, a simulated bootstrap estimate can be found, As an example, in the case of models of quanta! response which generalize the logistic model, see PREGIBON ( 1980 ), some data are analysed using the GLIM computer programKeywords
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