6533b824fe1ef96bd127fff5
RESEARCH PRODUCT
Fitting generalized linear models with unspecified link function: A P-spline approach
Giancarlo FerraraVito M. R. Muggeosubject
Statistics and ProbabilityGeneralized linear modelCanonical link elementApplied MathematicsLogitLinear modelRegression analysisLinear predictionProbitComputational MathematicsSpline (mathematics)Computational Theory and MathematicsStatisticsApplied mathematicsSettore SECS-S/01 - StatisticaGLM P-splines link function single index modelsMathematicsdescription
Generalized linear models (GLMs) outline a wide class of regression models where the effect of the explanatory variables on the mean of the response variable is modelled throughout the link function. The choice of the link function is typically overlooked in applications and the canonical link is commonly used. The estimation of GLMs with unspecified link function is discussed, where the linearity assumption between the link and the linear predictor is relaxed and the unspecified relationship is modelled flexibly by means of P-splines. An estimating algorithm is presented, alternating estimation of two working GLMs up to convergence. The method is applied to the analysis of quit behavior of production workers where the logit, probit and clog-log links do not appear to be appropriate.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2008-01-01 |