This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students.
This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students.
John Kolassa is Professor of Statistics and Biostatistics, Rutgers, the State University of New Jersey.
Inhaltsangabe
1. Background 2. One-Sample Nonparametric Inference 3. Two-Sample Testing 4. Methods for Three or More Groups 5. Group Differences with Blocking 6. Bivariate Methods 7. Multivariate Analysis 8. Density Estimation 9. Regression Function Estimates 10. Resampling Techniques Appendices
1. Background 2. One-Sample Nonparametric Inference 3. Two-Sample Testing 4. Methods for Three or More Groups 5. Group Differences with Blocking 6. Bivariate Methods 7. Multivariate Analysis 8. Density Estimation 9. Regression Function Estimates 10. Resampling Techniques Appendices
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