TY - JOUR
T1 - Nonparametric tilted density function estimation
T2 - a cross-validation criterion
AU - Doosti, Hassan
AU - Hall, Peter
AU - Mateu, Jorge
PY - 2018/12
Y1 - 2018/12
N2 - 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.
AB - 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.
KW - Cross validation function
KW - Non-parametric density function estimation
KW - Rate of convergence
KW - Tilted estimators
UR - http://www.scopus.com/inward/record.url?scp=85040511810&partnerID=8YFLogxK
U2 - 10.1016/j.jspi.2017.12.003
DO - 10.1016/j.jspi.2017.12.003
M3 - Article
AN - SCOPUS:85040511810
SN - 0378-3758
VL - 197
SP - 51
EP - 68
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
ER -