Semiparametric M-quantile regression for count data. (December 2014)

• Record Type: Journal Article
• Authors: Dreassi, Emanuela; Ranalli, M Giovanna; Salvati, Nicola
• Other Names: Lawson Andrew B guest-editor.; Ugarte María Dolores guest-editor.; MacNab Ying C guest-editor.; Lee Duncan guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 23:Number 6(2014:Dec.)
• Page Start: 591
• Page End: 610
• Publication Date: 2014-12
• Subjects: Disease mapping -- ecological regression -- geoadditive models -- Negative Binomial -- penalized splines -- robust method; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280214536636
• Languages: English
• ISSNs: 0962-2802

Abstract

Lung cancer incidence over 2005–2010 for 326 Local Authority Districts in England is investigated by ecological regression. Motivated from mis-specification of a Negative Binomial additive model, a semiparametric Negative Binomial M-quantile regression model is introduced. The additive part relates to those univariate or bivariate smoothing components, which are included in the model to capture nonlinearities in the predictor or to account for spatial dependence. All such components are estimated using penalized splines. The results show the capability of the semiparametric Negative Binomial M-quantile regression model to handle data with a strong spatial structure.