On the fourth-order hybrid beta polynomial kernels in kernel density estimation
This paper introduces a novel family of fourth-order hybrid beta polynomial kernels tailored for statistical analysis. The efficacy of these kernels is evaluated using two principal performance metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Comprehensive assessment...
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| Format: | Article |
| Language: | English |
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Nigerian Society of Physical Sciences
2024-02-01
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| Series: | Journal of Nigerian Society of Physical Sciences |
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| Online Access: | https://journal.nsps.org.ng/index.php/jnsps/article/view/1631 |
| _version_ | 1797304996608344064 |
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| author | Benson Ade Eniola Afere |
| author_facet | Benson Ade Eniola Afere |
| author_sort | Benson Ade Eniola Afere |
| collection | DOAJ |
| description |
This paper introduces a novel family of fourth-order hybrid beta polynomial kernels tailored for statistical analysis. The efficacy of these kernels is evaluated using two principal performance metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Comprehensive assessments were conducted using both simulated and real-world datasets, enabling a thorough comparison with conventional fourth-order polynomial kernels. The evaluation process entailed computing AMISE and efficiency metrics for both the hybrid and classical kernels. Consistently, the results illustrated the superior performance of the hybrid kernels over their classical counterparts across diverse datasets, underscoring the robustness and effectiveness of the hybrid approach. By leveraging these performance metrics and conducting evaluations on simulated and real world data, this study furnishes compelling evidence supporting the superiority of the proposed hybrid beta polynomial kernels. The heightened performance, evidenced by lower AMISE values and elevated efficiency scores, strongly advocates for the adoption of the proposed kernels in statistical analysis tasks, presenting a marked improvement over traditional kernels.
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| first_indexed | 2024-03-08T00:18:27Z |
| format | Article |
| id | doaj.art-d6b2c25b778f4eb09c5df1969443d62b |
| institution | Directory Open Access Journal |
| issn | 2714-2817 2714-4704 |
| language | English |
| last_indexed | 2024-03-08T00:18:27Z |
| publishDate | 2024-02-01 |
| publisher | Nigerian Society of Physical Sciences |
| record_format | Article |
| series | Journal of Nigerian Society of Physical Sciences |
| spelling | doaj.art-d6b2c25b778f4eb09c5df1969443d62b2024-02-16T16:31:23ZengNigerian Society of Physical SciencesJournal of Nigerian Society of Physical Sciences2714-28172714-47042024-02-016110.46481/jnsps.2024.1631On the fourth-order hybrid beta polynomial kernels in kernel density estimationBenson Ade Eniola Afere0Department of Mathematical Sciences, Faculty of Natural Sciences, Prince Abubakar Audu University, 272102, Anyigba, Nigeria. This paper introduces a novel family of fourth-order hybrid beta polynomial kernels tailored for statistical analysis. The efficacy of these kernels is evaluated using two principal performance metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Comprehensive assessments were conducted using both simulated and real-world datasets, enabling a thorough comparison with conventional fourth-order polynomial kernels. The evaluation process entailed computing AMISE and efficiency metrics for both the hybrid and classical kernels. Consistently, the results illustrated the superior performance of the hybrid kernels over their classical counterparts across diverse datasets, underscoring the robustness and effectiveness of the hybrid approach. By leveraging these performance metrics and conducting evaluations on simulated and real world data, this study furnishes compelling evidence supporting the superiority of the proposed hybrid beta polynomial kernels. The heightened performance, evidenced by lower AMISE values and elevated efficiency scores, strongly advocates for the adoption of the proposed kernels in statistical analysis tasks, presenting a marked improvement over traditional kernels. https://journal.nsps.org.ng/index.php/jnsps/article/view/1631Kernel density estimationFourth-order kernelshybrid kernelsAMISEEfficiency |
| spellingShingle | Benson Ade Eniola Afere On the fourth-order hybrid beta polynomial kernels in kernel density estimation Journal of Nigerian Society of Physical Sciences Kernel density estimation Fourth-order kernels hybrid kernels AMISE Efficiency |
| title | On the fourth-order hybrid beta polynomial kernels in kernel density estimation |
| title_full | On the fourth-order hybrid beta polynomial kernels in kernel density estimation |
| title_fullStr | On the fourth-order hybrid beta polynomial kernels in kernel density estimation |
| title_full_unstemmed | On the fourth-order hybrid beta polynomial kernels in kernel density estimation |
| title_short | On the fourth-order hybrid beta polynomial kernels in kernel density estimation |
| title_sort | on the fourth order hybrid beta polynomial kernels in kernel density estimation |
| topic | Kernel density estimation Fourth-order kernels hybrid kernels AMISE Efficiency |
| url | https://journal.nsps.org.ng/index.php/jnsps/article/view/1631 |
| work_keys_str_mv | AT bensonadeeniolaafere onthefourthorderhybridbetapolynomialkernelsinkerneldensityestimation |