View source for Recurrent Event Data Analysis

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Recurrent Event Data Analysis (RDA) is used in various applied fields such as reliability, medicine, social sciences, economics, business and criminology. Whereas in life data analysis (LDA) it was assumed that events (failures) were independent and identically distributed (iid), there are many cases where events are dependent and not identically distributed (such as repairable system data) or where the analyst is interested in modeling the number of occurrences of events over time rather than the length of time prior to the first event, as in LDA. 

Weibull++ provides both parametric and non-parametric approaches to analyze such data. 

*The [[Recurrent_Event_Data_Analysis#Non-Parametric_Recurrent_Event_Data_Analysis|non-parametric approach]] is based on the well-known Mean Cumulative Function (MCF). The Weibull++ module for this type of analysis builds upon the work of Dr. Wayne Nelson, who has written extensively on the calculation and applications of MCF [[Appendix:_Life_Data_Analysis_References|[31]]].

*The [[Recurrent_Event_Data_Analysis#Parametric_Recurrent_Event_Data_Analysis|parametric approach]] is based on the General Renewal Process (GRP) model, which is particularly useful in understanding the effects of the repairs on the age of a system. Traditionally, the commonly used models for analyzing repairable systems data are the perfect renewal processes (PRP), which corresponds to perfect repairs, and the nonhomogeneous Poisson processes (NHPP), which corresponds to minimal repairs. However, most repair activities may realistically not result in such extreme situations but in a complicated intermediate one (general repair or imperfect repair/maintenance), which are well treated with the GRP model.


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