Hierarchical Penalized Mixed Model

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dc.contributor.authorNdung’u, A. W.
dc.contributor.authorMwalili, S.
dc.contributor.authorOdongo, L.
dc.date.accessioned2021-09-17T09:31:23Z
dc.date.available2021-09-17T09:31:23Z
dc.date.issued2019
dc.descriptionA research article published in Open Journal of Statisticsen_US
dc.description.abstractPenalized spline has been a popular method for estimating an unknown function in the non-parametric regression due to their use of low-rank spline bases, which make computations tractable. However its performance is poor when estimating functions that are rapidly varying in some regions and are smooth in other regions. This is contributed by the use of a global smoothing parameter that provides a constant amount of smoothing across the function. In order to make this spline spatially adaptive we have introduced hierarchical penalized splines which are obtained by modelling the global smoothing parameter as another spline.en_US
dc.identifier.citationNdung’u, A.W., Mwalili, S. and Odongo, L. (2019) Hierarchical Penalized Mixed Model. Open Journal of Statistics, 9, 657-663.en_US
dc.identifier.issnOnline: 2161-7198
dc.identifier.issnPrint: 2161-718X
dc.identifier.urihttps://www.scirp.org/pdf/me_2019123012015757.pdf
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/22500
dc.language.isoenen_US
dc.publisherScientific Research Publishingen_US
dc.subjectPenalized Splinesen_US
dc.subjectMixed Modelen_US
dc.subjectSmoothing Parameteren_US
dc.titleHierarchical Penalized Mixed Modelen_US
dc.typeArticleen_US
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