Fitting generalized linear models with unspecified link function: a P-spline approach

DOI10.1016/j.csda.2007.08.011zbMath1452.62541OpenAlexW2057575413MaRDI QIDQ1023583

Vito M. R. Muggeo, Giancarlo Ferrara

Publication date: 12 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.08.011

zbMATH Keywords

B-splinesgeneralized linear modelslink functionpenalized likelihood

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Generalized linear models (logistic models) (62J12)

Related Items (11)

A new algorithm to estimate monotone nonparametric link functions and a comparison with parametric approach ⋮ Kumaraswamy regression model with Aranda-Ordaz link function ⋮ Generalized expectile regression with flexible response function ⋮ A robust P-spline approach to closed population capture-recapture models with time dependence and heterogeneity ⋮ Generalized additive models with unknown link function including variable selection ⋮ A penalized approach for the bivariate ordered logistic model with applications to social and medical data ⋮ Semiparametric stochastic frontier models: a generalized additive model approach ⋮ Semiparametric transformation models with Bayesian P-splines ⋮ Generalized additive models with flexible response functions ⋮ Nonparametric estimation of the link function including variable selection ⋮ Spatio-temporal expectile regression models

Uses Software

• SemiPar
• Fahrmeir

Cites Work

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