If an imputation is an intelligent guess about the value of a missing piece of information, this list might be interested in related methods that refrain from making any guesses at all about the missing information. A draft report on "interval statistics" is available at http://www.ramas.com/intstats.pdf that reviews basic descriptive statistics for data sets that contain intervals (rather than exclusively point values). It reviews methods to compute basic univariate descriptive statistics, including various means, the median and percentiles, variance, interquartile range, moments, confidence limits, and introduces the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report also explores the trade-off between measurement precision and sampling effort in statistical results that are sensitive to both, and considers the use of interval statistics as an alternative approach for the field of metrology.
I'd be very interested to hear your thoughts about this topic, including arguments that imputation procedures that generate specific values are better than interval statistics methods that don't. Best regards, Scott Scott Ferson [email protected] Applied Biomathematics 1-631-751-4350
