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The book uniquely compares eight linear discriminant functions (LDFs) across various datasets, including Fisher's iris data and medical data with collinearities. It introduces a 100-fold cross-validation method tailored for small samples and presents a straightforward model selection procedure to identify the optimal model based on minimum M2. The Revised IP-OLDF, evaluated using the MNM criterion, demonstrates superior performance compared to other M2s across the examined datasets, making it a significant contribution to statistical modeling and data analysis.
Nákup knihy
New Theory of Discriminant Analysis After R. Fisher, Shuichi Shinmura
- Jazyk
- Rok vydania
- 2018
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Platobné metódy
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- Jazyk
- anglicky
- Autori
- Shuichi Shinmura
- Vydavateľ
- Springer
- Rok vydania
- 2018
- Väzba
- mäkká
- Počet strán
- 228
- ISBN10
- 9811095469
- ISBN13
- 9789811095467
- Série
- Anotácia
- The book uniquely compares eight linear discriminant functions (LDFs) across various datasets, including Fisher's iris data and medical data with collinearities. It introduces a 100-fold cross-validation method tailored for small samples and presents a straightforward model selection procedure to identify the optimal model based on minimum M2. The Revised IP-OLDF, evaluated using the MNM criterion, demonstrates superior performance compared to other M2s across the examined datasets, making it a significant contribution to statistical modeling and data analysis.