519.5
Shinmura, Shuichi.
New Theory of Discriminant Analysis After R. Fisher Advanced Research by the Feature Selection Method for Microarray Data / by Shuichi Shinmura. -
XX, 208 p. 28 illus., 25 illus. in color. online resource.
Content notes : 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.
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* Mathematical statistics. Statistics. Statistical methods. Statistical Theory and Methods. Statistics for Life Sciences, Medicine, Health Sciences. Biostatistics. Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.