Analyzing data sets with missing data an empirical evaluation of imputation methods and likelihood-based methods.

Missing data are often encountered in data sets used to construct software effort prediction models. Thus far, the common practice has been to ignore observations with missing data. This may result in biased prediction models. The authors evaluate four missing data techniques (MDTs) in the context o...

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