| dc.contributor.author | Ndung’u, A. W. | |
| dc.contributor.author | Mwalili, S. | |
| dc.contributor.author | Odongo, L. | |
| dc.date.accessioned | 2021-09-17T09:31:23Z | |
| dc.date.available | 2021-09-17T09:31:23Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | Ndung’u, A.W., Mwalili, S. and Odongo, L. (2019) Hierarchical Penalized Mixed Model. Open Journal of Statistics, 9, 657-663. | en_US |
| dc.identifier.issn | Online: 2161-7198 | |
| dc.identifier.issn | Print: 2161-718X | |
| dc.identifier.uri | https://www.scirp.org/pdf/me_2019123012015757.pdf | |
| dc.identifier.uri | http://ir-library.ku.ac.ke/handle/123456789/22500 | |
| dc.description | A research article published in Open Journal of Statistics | en_US |
| dc.description.abstract | Penalized 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.language.iso | en | en_US |
| dc.publisher | Scientific Research Publishing | en_US |
| dc.subject | Penalized Splines | en_US |
| dc.subject | Mixed Model | en_US |
| dc.subject | Smoothing Parameter | en_US |
| dc.title | Hierarchical Penalized Mixed Model | en_US |
| dc.type | Article | en_US |
