Abstract
Ecological data can be difficult to collect, and as a
result, some important temporal ecological datasets
contain irregularly sampled data. Since many temporal
modelling techniques require regularly spaced data, one
common approach is to linearly interpolate the data,
and build a model from the interpolated data. However,
this process introduces an unquantified risk that the
data is over-fitted to the interpolated (and hence more
typical) instances. Using one such irregularly-sampled
dataset, the Lake Kasumigaura algal dataset, we compare
models built on the original sample data, and on the
interpolated data, to evaluate the risk of mis-fitting
based on the interpolated data.
Users
Please
log in to take part in the discussion (add own reviews or comments).