Abstract
In a previous simulation study, the complexity of neural networks for limited cases of binary and normally-distributed variables based the null distribution of the likelihood ratio statistic and the corresponding chi-square distribution was characterized. This study expands on those results and presents a more general formulation for calculating degrees of freedom.
| Original language | English |
|---|---|
| Pages (from-to) | 488-504 |
| Number of pages | 17 |
| Journal | Journal of Modern Applied Statistical Methods |
| Volume | 8 |
| Issue number | 2 |
| DOIs | |
| State | Published - Nov 2009 |
Keywords
- Chi-square distribution
- Degrees of freedom
- Null distribution
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