Analysis Of Overdispersion Data Using Poisson Invers Gaussian Regression Model
Abstract
Salah satu pelanggaran asumsi dalam model regresi Poisson pada kebanyakan data count (cacahan) adalah ditemukan kasus overdispersi, dimana varians variabel respon lebih besar dari rata-ratanya. Untuk mengatasi kasus overdispersi, dibentuk model regresi yang merupakan perpaduan antara distribusi Poisson dengan beberapa distribusi lain. Poisson Invers Gaussian (PIG) merupakan salah satu model regresi dari model campuran untuk mengatasi overdispersi. Penelitian ini membandingkan model regresi Poisson Invers Gaussian dengan model regresi Poisson bertujuan mengetahui variabel-variabel yang mempengaruhi jumlah kasus bayi mati di Provinsi Jawa Timur pada tahun 2019. Penaksiran parameter regresi PIG dilakukan dengan metode Maximum Likelihood Estimator (MLE). Berdasarkan nilai AIC paling kecil diketahui model regresi Poisson Invers Gaussian lebih baik dari model regresi Poisson.
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References
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