, Volume 25, Issue 4, pp 607–626

Statistical inference for generalized additive models: simultaneous confidence corridors and variable selection

  • Shuzhuan Zheng
  • Rong Liu
  • Lijian Yang
  • Wolfgang K. Härdle
Original Paper

DOI: 10.1007/s11749-016-0480-8

Cite this article as:
Zheng, S., Liu, R., Yang, L. et al. TEST (2016) 25: 607. doi:10.1007/s11749-016-0480-8


In spite of widespread use of generalized additive models (GAMs) to remedy the “curse of dimensionality”, there is no well-grounded methodology developed for simultaneous inference and variable selection for GAM in existing literature. However, both are essential in enhancing the capability of statistical models. To this end, we establish simultaneous confidence corridors (SCCs) and a type of Bayesian information criterion (BIC) through the spline-backfitted kernel smoothing techniques proposed in recent articles. To characterize the global features of each non-parametric components, SCCs are constructed for testing their overall trends and entire shapes. By extending the BIC in additive models with identity/trivial link, an asymptotically consistent BIC approach for variable selection is built up in GAM to improve the parsimony of model without loss of prediction accuracy. Simulations and a real example corroborate the above findings.


BIC Confidence corridor Extreme value Generalized additive mode Spline-backfitted kernel 

Mathematics Subject Classification

62G08 62G15 62G32 

Supplementary material

11749_2016_480_MOESM1_ESM.pdf (105 kb)
Supplementary material 1 (pdf 104 KB)

Copyright information

© Sociedad de Estadística e Investigación Operativa 2016

Authors and Affiliations

  • Shuzhuan Zheng
    • 1
    • 2
  • Rong Liu
    • 3
  • Lijian Yang
    • 4
  • Wolfgang K. Härdle
    • 5
    • 6
  1. 1.Center for Advanced Statistics and Econometrics ResearchSoochow UniversitySuzhouChina
  2. 2.Department of EconomicsColumbia UniversityNew YorkUSA
  3. 3.Department of Mathematics and StatisticsUniversity of ToledoToledoUSA
  4. 4.Center for Statistical Science and Department of Industrial EngineeringTsinghua UniversityBeijingChina
  5. 5.C.A.S.E.-Center for Applied Statistics and EconomicsHumboldt-Universität zu BerlinBerlinGermany
  6. 6.Lee Kong Chian School of Business, Sim Kee Boon Institute for Financial EconomicsSingapore Management UniversitySingaporeSingapore

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