New Theory of Discriminant Analysis After R. Fisher Advanced Research by the Feature Selection Method for Microarray Data /

• Κύριος συγγραφέας: Shinmura, Shuichi (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2016.
• Θέματα: Statistics.; Biostatistics.; Statistical Theory and Methods.; Statistics for Life Sciences, Medicine, Health Sciences.; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.

This is the first book to compare eight LDFs by different types of datasets, such as Fisher’s iris data, medical data with collinearities, Swiss banknote data that is a linearly separable data (LSD), student pass/fail determination using student attributes, 18 pass/fail determinations using exam sco...

Πίνακας περιεχομένων:

• 1 New Theory of Discriminant Analysis
• 1.1 Introduction
• 1.2 Motivation for our Research
• 1.3 Discriminant Functions
• 1.4 Unresolved Problem (Problem 1)
• 1.5 LSD Discrimination (Problem 2)
• 1.6 Generalized Inverse Matrices (Problem 3)
• 1.7 K-fold Cross-validation (Problem 4)
• 1.8 Matroska Feature Selection Method (Problem 5)
• 1.9 Summary
• References
• 2 Iris Data and Fisher’s Assumption
• 2.1 Introduction
• 2.2 Iris Data
• 2.3 Comparison of Seven LDFs
• 2.4 100-folf Cross-validation for Small Sample Method (Method 1)
• 2.5 Summary
• References
• 3 The Cephalo-Pelvic Disproportion (CPD) Data with Collinearity
• 3.1 Introduction
• 3.2 CPD Data
• 3.3 100-folf Cross-validation
• 3.4 Trial to Remove Collinearity
• 3.5 Summary
• References
• 4 Student Data and Problem 1
• 4.1 Introduction
• 4.2 Student Data
• 4.3 100-folf Cross-validation for Student Data
• 4.4 Student Linearly Separable Data
• 4.5 Summary
• References
• 5 The Pass/Fail Determination using Exam Scores -A Trivial Linear Discriminant Function
• 5.1 Introduction
• 5.2 Pass/Fail Determination by Exam Scores Data in 2012
• 5.3 Pass/Fail Determination by Exam Scores (50% Level in 2012)
• 5.4 Pass/Fail Determination by Exam Scores (90% Level in 2012)
• 5.5 Pass/Fail Determination by Exam Scores (10% Level in 2012)
• 5.6 Summary
• 6 Best Model for the Swiss Banknote Data – Explanation 1 of Matroska Feature -selection Method (Method 2) -. References
• 6 Best Model for Swiss Banknote Data
• 6.1 Introduction
• 6.2 Swiss Banknote Data
• 6.3 100-folf Cross-validation for Small Sample Method
• 6.4 Explanation 1 for Swiss Banknote Data
• 6.5 Summary
• References
• 7 Japanese Automobile Data – Explanation 2 of Matroska Feature Selection Method (Method 2)
• 7.1 Introduction
• 7.2 Japanese Automobile Data
• 7.3 100-folf Cross-validation (Method 1)
• 7.4 Matroska Feature Selection Method (Method 2)
• 7.5 Summary
• References
• 8 Matroska Feature Selection Method for Microarray Data (Method 2)
• 8.1 Introduction
• 8.2 Matroska Feature Selection Method (Method2)
• 8.3 Results of the Golub et al. Dataset
• 8.4 How to Analyze the First BGS
• 8.5 Statistical Analysis of SM1
• 8.6 Summary
• References
• 9 LINGO Program 1 of Method 1
• 9.1 Introduction
• 9.2 Natural (Mathematical) Notation by LINGO
• 9.3 Iris Data in Excel
• 9.4 Six LDFs by LINGO
• 9.5 Discrimination of Iris Data by LINGO
• 9.6 How to Generate Re-sampling Samples and Prepare Data in Excel File
• 9.7 Set Model by LINGO
• Index.