Journal of Fundamental Mathematics and Applications (JFMA)
Vol 2, No 2 (2019)

METODE MULTIPLE IMPUTATION UNTUK MENGATASI KOVARIAT TAK LENGKAP PADA DATA KEJADIAN BERULANG

Rianti Siswi Utami (Departemen Matematika, Universitas Gadjah Mada, Yogyakarta)
Danardono Danardono (Departemen Matematika, Universitas Gadjah Mada, Yogyakarta)

Article Info

Publish Date
30 Nov 2019

Abstract

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.

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Journal Info
Journal of Fundamental Mathematics and Applications (JFMA)
Website

Abbrev

jfma

Publisher

Universitas Diponegoro

Subject

Decision Sciences, Operations Research & Management

Description

Journal of Fundamental Mathematics and Applications (JFMA) is an Indonesian journal published by the Department of Mathematics, Diponegoro University, Semarang, Indonesia. JFMA has been published regularly in 2 scheduled times (June and November) every year. JFMA is established to highlight the ...