Analysis of recurrence data is useful for tracking or predicting the recurrence of events for units or systems being monitored over time. Such events can include product failure, repair, maintenance or even occurrences of disease or illness. For such analyses, it is usually more appropriate to use recurrence data analysis methods than to try to model the time between events.
While simple nonparametric statistical methods provide powerful tool for drawing conclusions from complicated data, parametric methods and methods for censored recurrence data allow extrapolation over time. View a video on this topic by Bill Meeker and a related JMP demo by Leo Wright.
This webcast is part of the Statistical Methods for Reliability with Dr. Meeker on-demand webcast series.