  We propose and implement a nonlinear Verification and Validation (V&V)
methodology to test two fitting procedures for the log-periodic power law model
(LPPL), a model that has diverse applications across data analysis, but known
estimation issues. Prior studies have focused on ex-post analyses of rare
events: Earthquakes, glacial break-off events, and financial crashes. Or, on
non-dynamical simulations such as additive noise or resampling. Our results
reject an estimation scheme that pre-conditions observed data by fitting and
removing an exponential trend. We validate a subordinated algorithm, and
confirm that it passes Feigenbaum's criticism, which articulates a broad hurdle
for ex-post statistical learning from rare events.
