| Format: | 1. Historical background. 2. Approaches to nonparametric density estimation. 3. Maximum likelihood density estimation. 4. Maximun penalized likelihood density estimation. 5. Discrete maximum penalized likelihood density estimation. 6. Nonparametric density estimation in higher dimensions. 7. Nonparametric regression of intensity function estimation. 8. Model building: speculative data analysis. Appendix I: an introduction to mathematical optimization theory. Appendix II: Numerical solution of constrained optimization problems. Appendix III: Optimization algorithms for noisy problems. Appendix IV: a brief primer in simulation. |
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