Nonparametric tilted density function estimation: A cross-validation criterion
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comunitat-uji-handle3:10234/8635
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https://doi.org/10.1016/j.jspi.2017.12.003 |
Metadatos
Título
Nonparametric tilted density function estimation: A cross-validation criterionFecha de publicación
2018-12Editor
ElsevierCita bibliográfica
DOOSTI, Hassan; HALL, Peter; MATEU, Jorge. Nonparametric tilted density function estimation: A cross-validation criterion. Journal of Statistical Planning and Inference, 2018, vol. 197, p. 51-68.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/science/article/pii/S037837581730215XVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
In this paper, we propose a tilted estimator for nonparametric estimation of a density function. We use a cross-validation criterion to choose both the bandwidth and the tilted estimator parameters. We demonstrate ... [+]
In this paper, we propose a tilted estimator for nonparametric estimation of a density function. We use a cross-validation criterion to choose both the bandwidth and the tilted estimator parameters. We demonstrate theoretically that our proposed estimator provides a convergence rate which is strictly faster than the usual rate attained using a conventional kernel estimator with a positive kernel. We investigate the performance through both theoretical and numerical studies. [-]
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