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Date:         Thu, 1 Dec 2005 21:22:57 -0500
Reply-To:     Peter Flom <flom@NDRI.ORG>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Peter Flom <flom@NDRI.ORG>
Subject:      Re: Multiple Imputation in a Narrow Range
Comments: To: topkatz@MSN.COM
Content-Type: text/plain; charset=US-ASCII

>>> Talbot Michael Katz <topkatz@MSN.COM> >>> wrote <<< There was a presentation at the most recent NYASUG meeting about robust regression, and the speaker was talking about dealing with various data issues. One issue in the example data (from a real experiment) was as follows. A measurement is done with a machine; readings are positive numbers, and the results above the machine calibration threshhold are lognormally distributed. Readings below the machine calibration threshhold are just reported as "< 0.4" or "< 0.6" (depending on how the machine was calibrated that particular day). The speaker had simply converted such values to 0.2 or 0.3, respectively, but invited suggestions from the crowd. One audience member (and SAS-L denizen) mentioned multiple imputation, and this idea led to a side discussion after the talk finished. These values are not true missing values; they are somewhere between 0 and the calibration threshhold, but they just can't be measured with the same accuracy as readings above the calibration threshhold, so there are no accurate values within that range. How would you handle such values? (Discarding is not an option.) >>>

The 'audience member and SAS-L denizen was me. Nice to meet you, TMK, and put a face to the name.

It was also an interesting discussion, and I look forward to more discussion of it here......


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