BOOTSTRAP CONFIDENCE INTERVAL ESTIMATION ON GEOMETRY PROCESS MODELS FOR LIFETIME DATA ANALYSIS A.S. Awalluddin (a*) N.A. Astuti (b) R. Cahyandari (c)
a, b, c) Departement of Mathematics, UIN Sunan Gunung Djati Bandung, Jl. A.H Nasution 105 Bandung
*aasolih[at]uinsgd.ac.id
Abstract
The geometric process is one of the renewal processes in the observation systems or components in the life time data analysis. This paper aims to introduce a geometric process model in the life test analysis under normal test conditions by determining the estimation of model parameters. The assumptions of data distribution used here are exponential and Weibull distribution, and the parameter estimation method used is maximum likelihood estimation (MLE). Confidence Interval (CI) estimation for each distribution uses CI percentile bootstrap
Keywords: geometric process- confidence interval boostrap - MLE - life time data analysis
