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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Many systems experience recurrent events. Recurrence data are collected to analyze quantities of interest, such as the mean cumulative number of events or the mean cumulative cost of events. Methods of analysis are available for recurrence data with left and/or right censoring. Due to practical constraints, however, recurrence data are sometimes recorded only in windows with gaps between the windows. This talk extends existing methods, both non-parametric and parametric, to window-observation recurrence data. The non-parametric estimator requires minimum assumptions, but will be biased if the size of the risk set is not positive over the entire period of interest. There is no such difficulty when using a parametric model for the recurrence data. For cases in which the size of the risk set is zero for some periods of time, we propose a simple method that uses a parametric adjustment to the non-parametric estimator. The methods are illustrated with numerical examples. (This is joint work with Jianying Zuo and William Q. Meeker.)
