Scoring rules give rise to methods for statistical inference and are useful tools to achieve robustness or reduce computations. Scoring rule inference is generally per- formed through first-order approximations to the distribution of the scoring rule es- timator or of the ratio-type statistic. In order to improve the accuracy of first-order methods even in simple models, we propose bootstrap adjustments of signed scoring rule root statistics for a scalar parameter of interest in presence of nuisance parameters. The method relies on the parametric bootstrap approach that avoids onerous calcula- tions specific of analytical adjustments. Numerical examples illustrate the accuracy of the proposed method.

Scoring rules give rise to methods for statistical inference and are useful tools to achieve robustness or reduce computations. Scoring rule inference is generally performed through first-order approximations to the distribution of the scoring rule estimator or of the ratio-type statistic. In order to improve the accuracy of first-order methods even in simple models, we propose bootstrap adjustments of signed scoring rule root statistics for a scalar parameter of interest in presence of nuisance parameters. The method relies on the parametric bootstrap approach that avoids onerous calculations specific of analytical adjustments. Numerical examples illustrate the accuracy of the proposed method.

Bootstrap adjustments of signed scoring rule root statistics

Mameli, Valentina;
2018-01-01

Abstract

Scoring rules give rise to methods for statistical inference and are useful tools to achieve robustness or reduce computations. Scoring rule inference is generally performed through first-order approximations to the distribution of the scoring rule estimator or of the ratio-type statistic. In order to improve the accuracy of first-order methods even in simple models, we propose bootstrap adjustments of signed scoring rule root statistics for a scalar parameter of interest in presence of nuisance parameters. The method relies on the parametric bootstrap approach that avoids onerous calculations specific of analytical adjustments. Numerical examples illustrate the accuracy of the proposed method.
File in questo prodotto:
File Dimensione Formato  
Mameli CSSC 2018.pdf

non disponibili

Descrizione: Articolo principale versione editore
Tipologia: Versione dell'editore
Licenza: Accesso chiuso-personale
Dimensione 358.36 kB
Formato Adobe PDF
358.36 kB Adobe PDF   Visualizza/Apri
MMV_revision.pdf

accesso aperto

Descrizione: Articolo principale post print
Tipologia: Documento in Post-print
Licenza: Accesso gratuito (solo visione)
Dimensione 216.33 kB
Formato Adobe PDF
216.33 kB Adobe PDF Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3685268
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 4
social impact