Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94459
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Yu, X | en_US |
dc.creator | Wang, C | en_US |
dc.creator | Yang, Z | en_US |
dc.creator | Jiang, B | en_US |
dc.date.accessioned | 2022-08-22T05:08:31Z | - |
dc.date.available | 2022-08-22T05:08:31Z | - |
dc.identifier.issn | 0943-4062 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/94459 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.rights | © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 | en_US |
dc.rights | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00180-022-01196-6. | en_US |
dc.subject | Bias reduction | en_US |
dc.subject | Kernel density estimation | en_US |
dc.subject | Point-wise estimator | en_US |
dc.subject | Tuning parameter selection | en_US |
dc.title | Tuning selection for two-scale kernel density estimators | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 2231 | en_US |
dc.identifier.epage | 2247 | en_US |
dc.identifier.volume | 37 | en_US |
dc.identifier.doi | 10.1007/s00180-022-01196-6 | en_US |
dcterms.abstract | Reducing the bias of kernel density estimators has been a classical topic in nonparametric statistics. Schucany and Sommers (1977) proposed a two-scale estimator which cancelled the lower order bias by subtracting an additional kernel density estimator with a different scale of bandwidth. Different from existing literatures that treat the scale parameter in the two-scale estimator as a static global parameter, in this paper we consider an adaptive scale (i.e., dependent on the data point) so that the theoretical mean squared error can be further reduced. Practically, both the bandwidth and the scale parameter would require tuning, using for example, cross validation. By minimizing the point-wise mean squared error, we derive an approximate equation for the optimal scale parameter, and correspondingly propose to determine the scale parameter by solving an estimated equation. As a result, the only parameter that requires tuning using cross validation is the bandwidth. Point-wise consistency of the proposed estimator for the optimal scale is established with further discussions. The promising performance of the two-scale estimator based on the adaptive variable scale is illustrated via numerical studies on density functions with different shapes. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Computational statistics, Nov. 2022, v. 37, p. 2231-2247 | en_US |
dcterms.isPartOf | Computational statistics | en_US |
dcterms.issued | 2022 | - |
dc.identifier.isi | WOS:000746797300001 | - |
dc.identifier.scopus | 2-s2.0-85123525691 | - |
dc.description.validate | 202208 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a1612, a2149a | - |
dc.identifier.SubFormID | 45613, 46790 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; NSFC 12001459 | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
twoscale.pdf | Pre-Published version | 887.82 kB | Adobe PDF | View/Open |
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