PENANGANAN OVERDISPERSI PADA PEMODELAN DATA CACAH DENGAN RESPON NOL BERLEBIH (ZERO-INFLATED)

Viarti Eminita, Anang Kurnia, Kusman Sadik

Abstract


Overdispersi pada data cacah yang disebabkan karena kasus nol berlebih tidak dapat ditangani dengan metode model linier umum biasa seperti Poisson dan Binomial Negatif. Penanganan overdispersi karena nol berlebih dapat dilakukan dengan menggunakan model Zero-Inflated. Zero-Inflated Poisson (ZIP) dan Zero-Inflated Binomial Negatif (ZIBN) telah diyakini performanya dalam menangani masalah ini. Selain menangani masalah tersebut kedua model ini juga dapat memberikan informasi mengenai penyebab nol berlebih pada data respon. Performa ke Empat model tersebut dibandingkan dalam menduga model dari jumlah anak yang tidak sekolah dalam keluarga di Provinsi Jawa Barat pada tahun 2017. Berdasarkan nilai dari ukuran Pearson Chi-Squares, Likelihood Ratio Chi-Square, dan Akaike Information Crieteria (AIC). Pearson Chi-Squares, model ZIP lebih baik dibandingkan ZIBN dan model lainnya, walaupun berbeda sedikit dengan ZIBN.

Keywords


Overdispersi, Zero-Inflated Poisson, Zero-Inflated Negative Binomial

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References


Hausman, J, BH. Hall and Z Griliches. 1984. “Econometric Models for Count Data with an Application to the Patents-R&D Relationship.” Econometrica.Vol. 52 (4), pp: 909-938.

Ismail, N and Abdul AJ. 2007. Handling Overdispersion with Negative Binomial and Generalized Poisson Regression Models. Virginia: Casualty Actuarial Society Forum, Winter 2007.

Jansakul N, Hinde JP. 2002. “Score Test for Zero-Inflated Poisson Models”. Computational Statistics and Data Analysis. Vol. 40 (1) :75-96.

Jeong, KM. 2017. “Modelling Count Responses with Overdispersion”. Communication of the Korean Statistical Society Vol. 19 (6), pp: 761-770.

Jiang, Y. and L. House. 2017. “Comparison of the Performance of Count Data Models under Different Zero-Inflation Scenarios Using Simulation Studies”. In 2017 Annual Meeting, July 30-August 1, 2017. Chicago. Agricultural & Applied Economics Association.

Lambert, D. 1992. “Zero-Inflated Poisson Regression with Application to Defects in Manufacturing”. Technometrics. Vol. 34 (1), pp: 1-14.

McCullagh, P. and J. Nelder. 1989. Generalized Linear Models (second ed.). London: Chapman and Hall.

Naya H, Urioste JI, Chang YM, Motta MR, Kremer R, Gianola D. 2008. “A comparison between Poisson and zero-inflated Poisson regression models with an application to number of black spots in Corriedale sheep”. Genetics Selection Evolution. Vol. 40 (4), pp: 379-394.

Nelder, J.A. and Wedderburn, R.W.M. 1972. “Generalized Linear Models”. Journal of the Royal Statistical Society, Series A. Vol. 135 (3), pp: 370-384.

Özdemir, T and Ecevit E. 2005. “Comparison of Chi-Square and Likelihood Ratio Chi-Square Tests: Power of Test”. Journal of Applied Sciences Research. Vol. 1 (2), pp: 242-244.

Palmgren, Juni. 1981. “The Fisher Information Matrix for Log-Linear Models Arguing Conditionally in the Observed Explanatory Variables”. Biometrika. Vol. 68 (2), pp: 563-566.

Zeiless et. al. 2008. “Regression Models for Count Data in R”. Journal of Statistical Software Vol. 27 (8), pp: 1-25.




DOI: https://doi.org/10.24853/fbc.5.1.71-80

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