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- W1508782493 abstract "Abstrak __________________________________________________________________________________________ Model mixture dapat mengestimasi proporsi pasien yang sembuh ( cured ) dan fungsi survival pasien tak sembuh ( uncured ). Pada kajian ini, model mixture dikembangkan untuk analisis cure rate berbasis missing data. Ada beberapa metode yang dapat digunakan untuk analisis missing data. Salah satu metode yang dapat digunakan adalah Algoritma EM, Metode ini didasarkan pada 2 (dua) langkah, yaitu: (1) Expectation Step dan (2) Maximization Step . Algoritma EM merupakan pendekatan iterasi untuk mempelajari model dari data dengan nilai hilang melalui 4 (empat) langkah, yaitu(1) pilih himpunan inisial dari parameter untuk sebuah model, (2) tentukan nilai ekspektasi untuk data hilang, (3) buat induksi parameter model baru dari gabungan nilai ekspekstasi dan data asli, dan (4) jika parameter tidak converged , ulangi langkah 2 menggunakan model baru. Berdasar kajian yang dilakukan dapat ditunjukkan bahwa pada algoritma EM, log-likelihood untuk missing data mengalami kenaikan setelah dilakukan setiap iterasi dari algoritmanya. Dengan demikian berdasar algoritma EM, barisan likelihood konvergen jika likelihood terbatas ke bawah. Abstract __________________________________________________________________________________________ Model mixture can estimate proportion of recovering patient and function of patient survival do not recover. At this study, model mixture developed to analyse cure rate bases on missing data. There are some method which applicable to analyse missing data. One of method which can be applied is Algoritma EM, This method based on 2 ( two) step, that is: ( 1) Expectation Step and ( 2) Maximization Step. EM Algorithm is approach of iteration to study model from data with value loses through 4 ( four) step, yaitu(1) select;chooses initial gathering from parameter for a model, ( 2) determines expectation value for data to lose, ( 3) induce newfangled parameter from value aliance ekspekstasi and original data, and ( 4) if parameter is not converged, repeats step of 2 to apply newfangled. Based on study done able to be indicated that to algorithm EM, log-likelihood for missing data experiences increase after done every iteration from its (the algorithm). Thereby based on algorithm EM, line likelihood convergent if limited likelihood downwards" @default.
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- W1508782493 date "2014-11-01" @default.
- W1508782493 modified "2023-10-05" @default.
- W1508782493 title "KONVERGENSI ESTIMATOR DALAM MODEL MIXTURE BERBASIS MISSING DATA" @default.
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