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

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Abstract

Generalized linear models (GLMs) outline a wide class of regression models where the effect of the explanatory variables on themean of the response variable is modelled throughout the link function. The choice of the link function is typically overlooked inapplications and the canonical link is commonly used. The estimation of GLMs with unspecified link function is discussed, wherethe linearity assumption between the link and the linear predictor is relaxed and the unspecified relationship is modelled flexiblyby 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.
Original languageEnglish
Pages (from-to)2529-2537
Number of pages9
JournalCOMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume52
Publication statusPublished - 2008

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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