We also showed that this new data distribution algorithm outperforms previous approaches (cyclic distribution of sites, monolithic distribution of partitions) in almost all cases with only minor deviations in cases where the performance was worse.
Moreover, in previous approaches, performances are directly related to the skewness of the input data distribution, while in our approach, the high throughput is independent from this factor.
The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct.
With DF approach, GE data distribution is not a problem, but computation would be enormously difficult, even with the help of using a set of starting value that could be a rough estimate at best from the LDA approach.
We therefore analyzed data by using both Student's t-test and the Fisher exact test, in which the first approach requires normal data distribution, whereas the second test does not have this requirement [ 45, 49, 50].
Bootstrap methods generally make no assumptions about data distribution; hence, they can be a useful approach when there are too few data to test or verify model assumptions.
One reason for choosing the logistic regression approach is that normality of the data distribution is not required.
There are two broad categories of ALS data analysis approaches: area based approaches (ABA)/statistical canopy height distribution approaches, and individual tree crown approaches (ITC).
A disadvantage for distribution-specific approaches is that unless prior knowledge is available, the data distribution should be tested.
First, a new synthetic oversampling approach called weighted minority oversampling (WMO) is devised to balance the data distribution.
