my first AAS. V. measurement error and EM

While discussing different view points on the term, clustering, one of the conversers led me to his colleague’s poster. This poster (I don’t remember its title and abstract) was my favorite from all posters in the meeting.

He rewrote the EM algorithm to include measurement errors in redshifts. Indexed parameters associated with different redshifts and corresponding standard deviations (measurement errors, treated as nuisance parameters) were included in the likelihood function that corrected bias and manifested bimodality in the LFs clearly at the different evolutionary stages.

I encouraged him to talk statisticians to characterize and to generalize his measurement error included likelihoods, and to optimize his EM algorithm. Because of approximations in algebra and the many parameters from measurement errors from redshifts, some assumptions and constraints were imposed intensively and I thought a collaboration with statisticians suits to get around constraints and to generalize his measurement error included likelihood.

One Comment
  1. jiangang:

    Thanks for the comments on my poster. Just clarify a little bit that the measurement error is not from redshift, but from color itself. That is, look for the intrinsic color distribution after correcting for the measurement errors on colors.

    10-14-2008, 1:00 am
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