Not exact matches
Availing of fraud protection services can help
automate this process, and using their advanced
algorithms, filter out fraudulent purchases while
ensuring real purchases get fulfilled.
With any
automated homogenization approach, it is critically important that the
algorithm be tested with synthetic data with various types of biases introduced (step changes, trend inhomogenities, sawtooth patterns, etc.), to
ensure that the
algorithm will identically deal with biases in both directions and not create any new systemic biases when correcting inhomogenities in the record.
The final post will examine
automated pairwise homogenization approaches in more detail, looking at how breakpoints are detected and how
algorithms can tested to
ensure that they are equally effective at removing both cooling and warming biases.