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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.
Buchkauf
New Theory of Discriminant Analysis After R. Fisher, Shuichi Shinmura
- Sprache
- Erscheinungsdatum
- 2017
- product-detail.submit-box.info.binding
- (Hardcover)
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- Titel
- New Theory of Discriminant Analysis After R. Fisher
- Untertitel
- Advanced Research by the Feature Selection Method for Microarray Data
- Sprache
- Englisch
- Autor*innen
- Shuichi Shinmura
- Verlag
- Springer
- Erscheinungsdatum
- 2017
- Einband
- Hardcover
- Seitenzahl
- 228
- ISBN13
- 9789811021633
- Reihe
- Schlagwörter
- Sachbücher, Wissenschaft & Mathematik, Mathematik
- Beschreibung
- 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.