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Reliability Analysis - Fit Competing Risk Mixture vs. Fit Mixture

Hello,

Interested in an example of when you would choose the Fit Competing Risk Mixture vs. Fit Mixture in the Life Distribution platform. Fit Competing Risk Mixture makes sense to think about multiple failure modes competing throughout the life of the product but I am uncertain as to what the use cases would be for the Fit Mixture. Is this a case where both should be fitted and AICc/BIC compared or is there only ever one of these tools that makes sense to apply?

Thank you!

Re: Reliability Analysis - Fit Competing Risk Mixture vs. Fit Mixture

Sounds like a job for global reliability expert, @DonMcCormack . 

Re: Reliability Analysis - Fit Competing Risk Mixture vs. Fit Mixture

Thanks @Phil_Kay. Try the attached attached ignoring the Cause column.  It is from Meeker & Escobar, p 383, Table 15.1. We've used it for one of the recorded sessions we did with Dr. Meeker. Unfortunately, I don't know if the recordings are still available. The data set won't necessarily give you insight into why you should choose one option over the other (I'll need to make one up for that), but it's a start since you have an explicit indication as to the failure cause.

Re: Reliability Analysis - Fit Competing Risk Mixture vs. Fit Mixture

Those recordings with Bill Meeker were great. Really good motivational stuff as well as the technical how-to.

Re: Reliability Analysis - Fit Competing Risk Mixture vs. Fit Mixture

Maybe @gail_massari knows if they are still available and where they can be found.

Re: Reliability Analysis - Fit Competing Risk Mixture vs. Fit Mixture

Thanks to everyone for the input! I had watched a few talks from Bill Meeker and Chris Gotwalt as a part of the Explorer series on the JMP website and those were great. Would be happy to know if there were additional talks to be found somewhere.

Best Practices in Reliability Data Analysis | On-Demand Webinars | JMP

Re: Reliability Analysis - Fit Competing Risk Mixture vs. Fit Mixture

I am not sure if you got an answer yet. Basically, the mixture model is an investigative approach when you do not have assignable failure modes but you believe that more than one of them are active in the single set of lifetime data. You can explore a mixture of models to determine the number of failure modes and begin to understand their hazards.

This case is similar to unsupervised learning in predictive modeling.