Multiple imputation is one of estimation method used to impute missing
observations. This method imputes missing observation several times then it is more
possible to get the right estimate than just one time imputation. In this research, the
method will be applied to estimate missing observations in covariates of recurrent event
data. Some multiple imputation methods will be considered including combination of
the event indicator, the event times, the logarithm of event times, and the cumulative
baseline hazard. To compare these methods, Monte Carlo simulation will be used based
on relative bias and Mean Squared Error (MSE). The recurrent events will be modelled
using Cox proportional hazard model. Furthermore, real data application will be
presented.
Rumpun Ilmu
Statistik
Bahasa Asli/Original Language
Bahasa Indonesia
Level
Nasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
Metode Multiple Imputation untuk Mengatasi Kovariat Tak Lengkap pada Data Kejadian Berulang.PDF
[PAK] Full Dokumen
2
Editorial Team.pdf
[PAK] Halaman Editorial
3
Journal of Fundamental Mathematics and Applications (JFMA).pdf
[PAK] Halaman Cover
4
Table of Contents Vol 2, No 2 (2019).pdf
[PAK] Daftar Isi
5
Bukti korespondensi paper karya ilmiah_Rianti Siswi Utami.pdf
