Abstract
The term survival analysis has been used in a broad sense to describe collection of statistical procedures for data analysis for which the outcome variable of interest is time until an event occurs, the time to failure of an experimental unit might be censored and this can be right, left, interval, and Partly Interval Censored data (PIC). In this paper, the analysis of this model is conducted based on parametric Weibull model via PIC data. Moreover, two imputation techniques are used, which are: left point and right point. The effectiveness of the proposed model is tested through numerical analysis on simulated and secondary data sets.
Highlights
Statistical method is one of the strategies used by researchers as it provides various kinds of methods in analyzing data
There are different reasons for censoring which lead to different types of censored data. These are right, left, interval, and one of the most important types of interval censored data is partly interval censored data which means that for some of the subjects the event of interest is exactly observed while for others it lies within an interval (Kim (2003))
Parametric Weibull Model Based on Imputations Techniques for Partly Interval Censored data (PIC) Data
Summary
Statistical method is one of the strategies used by researchers as it provides various kinds of methods in analyzing data. Survival analysis or failure time analysis is described as one of the most significant and advanced methods in statistics during the last quarter of the 20th century (Sam and Krings (2008)). As mentioned by Liu (2012), one of the examples of engineering application that deals with the survival analysis method is the testing of life time or durability of a mechanical or an electrical component. Scientists apply this technique to track the products and material’s life span for predicting the product reliability and durability. Analysis is conducted based on partly interval censored via simulated and secondary medical data
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