| dc.contributor.author | Obudho, Macdonald G. | |
| dc.contributor.author | Orwa, George O. | |
| dc.contributor.author | Otieno, Romanus Odhiambo | |
| dc.contributor.author | Were, Festus A. | |
| dc.date.accessioned | 2022-10-17T11:53:07Z | |
| dc.date.available | 2022-10-17T11:53:07Z | |
| dc.date.issued | 2022-10-11 | |
| dc.identifier.citation | Obudho, M.G., Orwa, G.O., Otieno, R.O. and Were, F.A. (2022) Classification of Stateless People through a Robust Nonparametric Kernel Discriminant Function. Open Journal of Statistics, 12, 563-580. | en_US |
| dc.identifier.uri | https://doi.org/10.4236/ojs.2022.125034 | |
| dc.identifier.uri | http://repository.must.ac.ke/handle/123456789/789 | |
| dc.description.abstract | Statelessness is the absence of any Nationality. These include the Pemba, Shona, Galjeel, people of Burundi and Rwanda descent, and children born in Kenya to British Overseas Citizens after 1983. Frequently, they are not only undocumented but also often overlooked and not included in National Administrative Registers. Accordingly, find it hard to participate in Social and Economic Affairs. There has been a major push by UNHCR and international partners to “map” the size of stateless populations and their demographic profile, as well as causes, potential solutions and human rights situation. One of the requirements by the UNHCR in their push is for countries to find a potential solution to statelessness which starts with classifying/associating a person from these communities to a particular local community that is recognized in Kenya. This paper addresses this problem by adopting a Robust Nonparametric Kernel Discriminant function to correctly classify the stateless communities in Kenya and compare the performance of this method with the existing techniques through their classification rates. This is because Nonparametric functions have proven to be more robust and useful especially when there exists auxiliary information which can be used to increase precision. The findings from this paper indicate that Nonparametric discriminant classifiers provide a good classification method for classifying the stateless communities in Kenya. This is because they exhibit lower classification rates compared to the parametric methods such as Linear and Quadratic discriminant functions. In addition, the finding shows that based on certain similarities in characteristics that exist in these communities that surround the Pemba Community, the Pemba community can be classified as Giriama or Rabai in which they seem to have a strong link. In this regard, the study recommends the use of the Kernel discriminant classifiers in classifying the stateless persons and that the Government of Kenya consider integrating/recognizing the Pemba community into Giriama or Rabai so that they can be issued with the National Identification Cards and be recognized as Kenyans. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Scientific Research Publishing | en_US |
| dc.subject | Discriminant Analysis | en_US |
| dc.subject | Kernel Discriminant | en_US |
| dc.subject | Nonparametric | en_US |
| dc.subject | Classification | en_US |
| dc.subject | Statelessness | en_US |
| dc.title | Classification of Stateless People through a Robust Nonparametric Kernel Discriminant Function | en_US |
| dc.type | Article | en_US |
