Statistical Methods of Discrimination and Classification
Advances in Theory and Applications
- 1st Edition - January 1, 1986
- Latest edition
- Editors: Sung C. Choi, Ervin Y. Rodin
- Language: English
Statistical Methods of Discrimination and Classification: Advances in Theory and Applications is a collection of papers that tackles the multivariate problems of discriminating and… Read more
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Description
Description
Statistical Methods of Discrimination and Classification: Advances in Theory and Applications is a collection of papers that tackles the multivariate problems of discriminating and classifying subjects into exclusive population. The book presents 13 papers that cover that advancement in the statistical procedure of discriminating and classifying. The studies in the text primarily focus on various methods of discriminating and classifying variables, such as multiple discriminant analysis in the presence of mixed continuous and categorical data; choice of the smoothing parameter and efficiency of k-nearest neighbor classification; and assessing the performance of an allocation rule. The book will be of great use to researchers and practitioners of wide array of scientific disciplines, including engineering, psychology, biology, and physics.
Table of contents
Table of contents
Foreword
Discrimination and Classification: Overview
Multiple Discriminant Analysis in the Presence of Mixed Continuous and Categorical Data
On the Estimation of the Expected Probability of Misclassification in Discriminant Analysis with Mixed Binary and Continuous Variables
Parametric and Kernel Density Methods in Discriminant Analysis: Another Comparison
Multiple Group Logistic Discrimination
Distribution-Free Partial Discrimination Procedures
Choice of the Smoothing Parameter and Efficiency of Â:-Nearest Neighbor Classification
Monte Carlo Study of Forward Stepwise Discrimination Based on Small Samples
The Robust Estimation of Classification Error Rates
Assessing the Performance of an Allocation Rule
The Variance of the Error Rates of Classification Rules
Estimating Class Sizes by Adjusting Fallible Classifier Results
On a Classification Rule for Multiple Measurements
Product details
Product details
- Edition: 1
- Latest edition
- Published: May 17, 2014
- Language: English
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