Mathematics and Statistics Vol. 1(2), pp. 64 - 73
DOI: 10.13189/ms.2013.010209
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Representing Data Distributions with a Nonparametric Kernel Density: The Way to Estimate the Optimal Oil Contents of Palm Mesocarp at Various Periods


Divo Dharma Silalahi1,2,*, Putri Aulia Wahyuningsih1, Fahri Arief Siregar1
1 SMART Research Institute, PT SMART Tbk, Riau, Indonesia
2 Graduate Students, Institute of Statistics, University of The Philippines Los BaƱos, Philippines

ABSTRACT

The most popular nonparametric density estimates is kernel density estimate. This estimate depends on the bandwidth choice which was given the optimization to kernel optimality process. We proposed Epanechnikov kernel which is the most optimal kernel in the AMISE. The resample data as replicate samples has been obtained by using bootstrap mechanism to provide the information about the sampling distribution. Then the resample data was used in Epanechnikov kernel simulation to estimate the optimal solution. This study was simulated using oil contents (%) data at various periods after pollination. The oil contents (%) were obtained by extraction of oil palm mesocarp. The result show that, Epanechnikov kernel using resamples data from bootstrap could be used for nonparametric optimization cases such as oil content (%) of oil palm mesocarp.

KEYWORDS
Nonparametric, Epanechnikov Kernel, Density Estimation, Bootstrap, Optimization, Oil Palm Mesocarp

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Divo Dharma Silalahi , Putri Aulia Wahyuningsih , Fahri Arief Siregar , "Representing Data Distributions with a Nonparametric Kernel Density: The Way to Estimate the Optimal Oil Contents of Palm Mesocarp at Various Periods," Mathematics and Statistics, Vol. 1, No. 2, pp. 64 - 73, 2013. DOI: 10.13189/ms.2013.010209.

(b). APA Format:
Divo Dharma Silalahi , Putri Aulia Wahyuningsih , Fahri Arief Siregar (2013). Representing Data Distributions with a Nonparametric Kernel Density: The Way to Estimate the Optimal Oil Contents of Palm Mesocarp at Various Periods. Mathematics and Statistics, 1(2), 64 - 73. DOI: 10.13189/ms.2013.010209.