KONEČNÁ, Kateřina. Priestley-Chao Estimator of Conditional Density. Online. In Jaromír Baštinec, Miroslav Hrubý. Mathematics, Information Technologies and Applied Sciences 2017, post-conference proceedings of extended versions of selected papers. Brno: University of Defence, Brno, 2017, 2017, s. 151-163. ISBN 978-80-7582-026-6. |
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@inproceedings{1400340, author = {Konečná, Kateřina}, address = {Brno}, booktitle = {Mathematics, Information Technologies and Applied Sciences 2017, post-conference proceedings of extended versions of selected papers}, editor = {Jaromír Baštinec, Miroslav Hrubý}, keywords = {kernel smoothing; conditional density; Priestley-Chao estimator; statistical properties; bandwidth selection; cross-validation method}, howpublished = {elektronická verze "online"}, language = {eng}, location = {Brno}, isbn = {978-80-7582-026-6}, pages = {151-163}, publisher = {University of Defence, Brno, 2017}, title = {Priestley-Chao Estimator of Conditional Density}, url = {http://mitav.unob.cz/data/MITAV%202017%20Proceedings.pdf}, year = {2017} }
TY - JOUR ID - 1400340 AU - Konečná, Kateřina PY - 2017 TI - Priestley-Chao Estimator of Conditional Density PB - University of Defence, Brno, 2017 CY - Brno SN - 9788075820266 KW - kernel smoothing KW - conditional density KW - Priestley-Chao estimator KW - statistical properties KW - bandwidth selection KW - cross-validation method UR - http://mitav.unob.cz/data/MITAV%202017%20Proceedings.pdf N2 - This contribution is focused on a non-parametric estimation of conditional density. Several types of kernel estimators of conditional density are known, the Nadaraya-Watson and the local linear estimators are the widest used ones. We focus on a new estimator - the Priestley-Chao estimator of conditional density. As conditional density can be regarded as a generalization of regression, the Priestley-Chao estimator, proposed initially for kernel regression, is extended for kernel estimation of conditional density. The conditional characteristics and the statistical properties of the suggested estimator are derived. The estimator depends on the smoothing parameters called bandwidths which influence the final quality of the estimate significantly. The cross-validation method is suggested for their estimation and the expression for the cross-validation function is derived. ER -
KONEČNÁ, Kateřina. Priestley-Chao Estimator of Conditional Density. Online. In Jaromír Baštinec, Miroslav Hrubý. \textit{Mathematics, Information Technologies and Applied Sciences 2017, post-conference proceedings of extended versions of selected papers}. Brno: University of Defence, Brno, 2017, 2017, s.~151-163. ISBN~978-80-7582-026-6.
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