Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation

Based on theoretical arguments and empirical evidence we advocate the use of the lognormal distribution as a model for activity times. However, raw data on activity times are often subject to rounding and to the Parkinson effect. We address those factors in our statistical tests by using a generalized version of the Parkinson distribution with random censoring of earliness, ultimately validating our model with field data from several sources. We also confirm that project activities exhibit stochastic dependence that can be modeled by linear association.

Highlight
► We validate the lognormal distribution for project activity times.
► We introduce a new version of the Parkinson distribution (hidden earliness).
► We show how use it to diagnose and estimate the Parkinson effect.
► We demonstrate that activity times are usually correlated and often uncalibrated.
► We show how to account for correlation and calibration by a linear association model.