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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.
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New Theory of Discriminant Analysis After R. Fisher, Shuichi Shinmura
- Taal
- Jaar van publicatie
- 2018
- product-detail.submit-box.info.binding
- (Paperback)
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- Taal
- Engels
- Auteurs
- Shuichi Shinmura
- Uitgever
- Springer
- Jaar van publicatie
- 2018
- Formaat
- Paperback
- Aantal pagina's
- 228
- ISBN10
- 9811095469
- ISBN13
- 9789811095467
- Reeks
- Aantekening
- 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.