Median bias reduction in cumulative link models

• Vincenzo Gioia ^[1] ; Euloge Clovis Kenne Pagui ; Alessandra Salvan
  1. [1] Università di Udine

     Università di Udine

     Udine, Italia

     
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 102-107
• Idioma: inglés
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• Resumen

  • For cumulative link models, we propose a new estimation approach aiming at median bias reduction (Kenne Pagui et al., 2017). Such approach is based on an adjustment of the score function. The method does not require finiteness of the maximum likelihood estimate and is effective in preventing boundary estimates. The resulting estimator is componentwise third-order median unbiased in the continuous case and equivariant under componentwise monotone reparameterizations. Simulation studies and an application compare the proposed method with maximum likelihood and mean bias reduction.