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Double one‐sided cross‐validation of local linear hazards
Author(s) -
Gámiz María Luz,
Mammen Enno,
Miranda María Dolores Martínez,
Nielsen Jens Perch
Publication year - 2016
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12133
Subject(s) - weighting , estimator , smoothing , cross validation , kernel smoother , kernel (algebra) , hazard , bandwidth (computing) , computer science , mathematics , mathematical optimization , kernel method , statistics , artificial intelligence , physics , discrete mathematics , support vector machine , computer network , chemistry , organic chemistry , radial basis function kernel , acoustics
Summary The paper brings together the theory and practice of local linear kernel hazard estimation. Bandwidth selection is fully analysed, including double one‐sided cross‐validation that is shown to have good practical and theoretical properties. Insight is provided into the choice of the weighting function in the local linear minimization and it is pointed out that classical weighting sometimes lacks stability. A new semiparametric hazard estimator transforming the survival data before smoothing is introduced and shown to have good practical properties.