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It is well known that this estimator is very sensitive to departures from the Gaussian data assumption.
In image processing with a short finite data, assumption of a power spectrum with known characteristics is generally not possible.
Although the approach establishes that a relationship for a historical data set existing within period n, it cannot be directly employed in forecasting for periods existing outside the historical data window due to the time-invariant data assumption made.
Third, the non-negative data assumption in Hozo et al.'s method is also quite restrictive.
Sensitivity analyses will be conducted to assess the robustness of the missing data assumption made in the primary analysis.
A sensitivity analysis to this missing data assumption has been undertaken using a pro-rata missing value score for the physical items.
Similar(30)
During the last 5 years, some data, assumptions, and models have been improved and altered significantly.
Moreover, indicators have the potential to be misleading, if the data, assumptions, or analyses behind them are incorrect.
This unexpected result was verified by Monte Carlo simulation of several different models, using both normally and lognormally distributed data assumptions.
Finally, two applied examples are used to demonstrate the sensitivity of inferences under different missing data assumptions and how this may change the substantive interpretation.
Requiring less strict data assumptions also makes it more practical.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com