Electrical data processing techniques are described for performing
business analysis based on datasets that are incomplete (e.g., contain
censored data) and/or based on datasets that are derived from a
stage-based business operation. A first technique offsets the effects of
error caused by the incomplete dataset by performing a trending operation
followed by a de-trending operation. A second technique provides a model
containing multiple sub-models, where the output of one sub-model serves
as the input to another sub-model in recursive fashion. A third technique
determines when a specified event is likely to occur with respect to a
given asset by first discriminating whether the event is very unlikely to
occur; if the asset does not meet this initial test, it is further
processed by a second sub-model, which determines the probability that
the specified event will occur for each of a specified series of time
intervals.