Jocelyn E Holden
Biographic Data
| ID | 9252883 |
|---|---|
| NAME | Jocelyn E Holden |
| GIVEN NAMES | Jocelyn E |
| FAMILY NAME | Holden |
| SIGNATURE | HOLDEN J E |
| AFFILIATIONS | Indiana University Bloomington |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2016 |
| H-INDEX | 0 |
Multilevel Modeling Using R
Multilevel Modelling using R provides a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent vari…
A Comparison of Two-Group Classification Methods
The statistical classification of N individuals into G mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis, quadratic discriminant analysis, and logistic regression. However, recent developments in the statistics…
The Effects of Initially Misclassified Data on the Effectiveness of Discriminant Function Analysis and Finite Mixture Modeling
Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture models (FMM) do not require, or even use if available, knowledge of group status to estimate paramete…
No prominent works on this page.
The Effects of Initially Misclassified Data on the Effectiveness of Discriminant Function Analysis and Finite Mixture Modeling
Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture models (FMM) do not require, or even use if available, knowledge of group status to estimate paramete…
A Comparison of Two-Group Classification Methods
The statistical classification of N individuals into G mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis, quadratic discriminant analysis, and logistic regression. However, recent developments in the statistics…
Multilevel Modeling Using R
Multilevel Modelling using R provides a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent vari…
Computer Science (3 works) · Advanced Statistical Methods and Models (2 works) · Artificial Intelligence (2 works) · Artificial Intelligence (2 works) · Discriminant (2 works) · Linear discriminant analysis (2 works) · Machine learning (2 works) · Mathematics (2 works) · Quadratic classifier (2 works) · Statistics (2 works)