Authors:

LUH GEDE UDAYANI, I WAYAN SUMARJAYA, MADE SUSILAWATI

Abstract:

“Dependence relationship between two or more variables is an issue that is often studied in the science of probability and statistics. Pearson correlation is often the easiest option to measure dependencies between variables. It is well known, that Pearson correlation assumes that the variable under study must be normally distributed. However, in reality this is not the case; for example, data in fields such as climatology and meteorology, insurance, and financial. Copula is a tool that can be used to model the joint distribution because it does not require the assumption of normality of the data so that it is resilient against a wide range of data. In this study, we discussed the application of copula in modeling the structure of dependencies between two variables: the production of rice and El-Nino Southern Oscillation (ENSO) indicator per period in Tabanan Regency. The best dependency model structure is given by the Frank copula of the Archimedean copula family with estimation parameter, ? = 2,817 and the loglikelihood value of 3,47.”

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PDF:

https://jurnal.harianregional.com/mtk/full-25319

Published

2016-11-30

How To Cite

UDAYANI, LUH GEDE; SUMARJAYA, I WAYAN; SUSILAWATI, MADE. ANALISIS HUBUNGAN PRODUKSI PADI DAN INDIKATOR ENSO DI KABUPATEN TABANAN DENGAN PENDEKATAN COPULA.E-Jurnal Matematika, [S.l.], v. 5, n. 4, p. 164-169, nov. 2016. ISSN 2303-1751. Available at: https://jurnal.harianregional.com/mtk/id-25319. Date accessed: 28 Aug. 2025. doi:https://doi.org/10.24843/MTK.2016.v05.i04.p136.

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Issue

Vol 5 No 4 (2016)

Section

Articles

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