Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/132719
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dc.titleImproving bandwidth selection methods by adding qualitative constraints
dc.contributor.authorFutschik, A.
dc.contributor.authorClarke, B.R.
dc.date.accessioned2016-12-13T05:35:43Z
dc.date.available2016-12-13T05:35:43Z
dc.date.issued2004
dc.identifier.citationFutschik, A., Clarke, B.R. (2004). Improving bandwidth selection methods by adding qualitative constraints. Computational Statistics 19 (3) : 445-453. ScholarBank@NUS Repository.
dc.identifier.issn09434062
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/132719
dc.description.abstractIn the context of nonparametric density estimation, we consider the combination of automatic bandwidth selection rules with qualitative constraints on the search space of bandwidths derived from bounds on the number of modes. These constraints can be easily combined with an upper bound based on the concept of oversmoothing introduced by Terrell and Scott (1985). Rather obviously, if a correct upper bound on the number of modes is known, our proposed approach helps to ensure an adequate representation of known qualitative features by the estimate. More surprisingly, even loose upper bounds on the number of modes are able to improve the MISE behavior of least squares cross-validation, by reducing the known tendency of under-smoothing of this bandwidth selector.
dc.sourceScopus
dc.subjectBandwidth selection
dc.subjectCross-validation
dc.subjectDensity estimation
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleComputational Statistics
dc.description.volume19
dc.description.issue3
dc.description.page445-453
dc.identifier.isiutNOT_IN_WOS
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