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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.
Zakup książki
New Theory of Discriminant Analysis After R. Fisher, Shuichi Shinmura
- Język
- Rok wydania
- 2017
- Oprawa
- (twarda)
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- Tytuł
- New Theory of Discriminant Analysis After R. Fisher
- Podtytuł
- Advanced Research by the Feature Selection Method for Microarray Data
- Język
- angielski
- Autorzy
- Shuichi Shinmura
- Wydawca
- Springer
- Rok wydania
- 2017
- Oprawa
- twarda
- Liczba stron
- 228
- ISBN13
- 9789811021633
- Seria
- Tagi
- Opis
- 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.