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Titolo:
POISSON VS NORMAL-ERRORS REGRESSION IN MACNALLY (1996)
Autore:
CANDY SG;
Indirizzi:
FORESTRY TASMANIA,GPO BOX 207B HOBART TAS 7001 AUSTRALIA
Titolo Testata:
Australian journal of ecology
fascicolo: 2, volume: 22, anno: 1997,
pagine: 233 - 235
SICI:
0307-692X(1997)22:2<233:PVNRIM>2.0.ZU;2-P
Fonte:
ISI
Lingua:
ENG
Soggetto:
GENERALIZED LINEAR-MODELS; MIXED MODELS;
Keywords:
GENERALIZED LINEAR MODELS; GOODNESS-OF-FIT STATISTICS; POISSON DISTRIBUTION; ROOT-MEAN SQUARE PREDICTION ERROR;
Tipo documento:
Editorial Material
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
11
Recensione:
Indirizzi per estratti:
Citazione:
S.G. Candy, "POISSON VS NORMAL-ERRORS REGRESSION IN MACNALLY (1996)", Australian journal of ecology, 22(2), 1997, pp. 233-235

Abstract

Mac Nally (1996), in describing the application of 'hierarchical partitioning' in regression modelling of species richness of breeding passerine birds with response variable the species count, rejects the use of Poisson regression in favour of normal-errors regression on an incorrect basis. Mac Nally uses a function of the residual sum of squares,the root-mean square prediction error (RMSPE), calculated from predictions from each regression and rejects the Poisson regression because its RMSPE was 20% larger. This note points out that the RMSPE will always be larger for the Poisson regression, given the same link functionand linear predictor is used, even if the response is truly Poisson. References to appropriate methods of determining the most suitable response distribution and link function in the context of generalized linear models are given.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 11/07/20 alle ore 07:46:19