Jackknifing & Bayesian rmANOVA

Comments

• EJ

  July 2022

  Hi Laura

  The jackknife is a resampling technique, right? When you're doing a Bayesian analysis that is not needed. But I guess I should look up the Kiesel paper...

  Cheers,

  E.J.
• laiskl

  July 2022

  Dear E.J.,

  yes, that's right, jackknifing is a resampling technique. When you say, that is not needed when doing a Bayesian analysis, do you mean the correction (i.e. analogous to the correction of an F-value in a frequentist analysis) is not necessary or are you referring to the resampling itself?

  Maybe to clarify what I am doing: I am interested in when a certain event-related potential (ERP) component occurs and I expect differences between my experimental conditions. EEG latency measures are really sensitive to noise - that's why it is usually recommended to use jackknifing. Essentially, that means rather than obtaining one measure per subject from the ERP belonging to that one subject, I create a series of average waveforms, each of which is missing one subject (leave-one-out-averages). Those leave-one-out-averages will only be slightly different from each other, reflecting the subject who was omitted from the average. Because the waveforms (and the resulting latency measures) are very similar, the error variance has been artifically reduced. Thus, in a convential ANOVA I divide the F-value by (N-1)^2 to correct for this reduction in variance. Does that make sense?

  Best,

  Laura
• EJ

  July 2022

  Dear Laura,

  I am referring to resampling itself. I do realize the resampling is handy in the present context, because otherwise you need some sort of complicated time series model (which will almost certainly be misspecified). This is definitely interesting but I cannot provide a confident answer at this point. But I am confident that it would be really nice to figure this out!

  Cheers,

  E.J.
• laiskl

  August 2022 edited August 2022

  Thanks anyway for your time to look into this!

  Best,

  Laura