Author | Simonoff, Jeffrey S. author |
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Title | Smoothing Methods in Statistics [electronic resource] / by Jeffrey S. Simonoff |
Imprint | New York, NY : Springer New York, 1996 |
Connect to | http://dx.doi.org/10.1007/978-1-4612-4026-6 |
Descript | XII, 340 p. online resource |
1. Introduction -- 1.1 Smoothing Methods: a Nonparametric/Parametric Compromise -- 1.2 Uses of Smoothing Methods -- 1.3 Outline of the Chapters -- Background material -- Computational issues -- Exercises -- 2. Simple Univariate Density Estimation -- 2.1 The Histogram -- 2.2 The Frequency Polygon -- 2.3 Varying the Bin Width -- 2.4 The Effectiveness of Simple Density Estimators -- Background material -- Computational issues -- Exercises -- 3. Smoother Univariate Density Estimation -- 3.1 Kernel Density Estimation -- 3.2 Problems with Kernel Density Estimation -- 3.3 Adjustments and Improvements to Kernel Density Estimation -- 3.4 Local Likelihood Estimation -- 3.5 Roughness Penalty and Spline-Based Methods -- 3.6 Comparison of Univariate Density Estimators -- Background material -- Computational issues -- Exercises -- 4. Multivariate Density Estimation -- 4.1 Simple Density Estimation Methods -- 4.2 Kernel Density Estimation -- 4.3 Other Estimators -- 4.4 Dimension Reduction and Projection Pursuit -- 4.5 The State of Multivariate Density Estimation -- Background material -- Computational issues -- Exercises -- 5. Nonparametrie Regression -- 5.1 Scatter Plot Smoothing and Kernel Regression -- 5.2 Local Polynomial Regression -- 5.3 Bandwidth Selection -- 5.4 Locally Varying the Bandwidth -- 5.5 Outliers and Autocorrelation -- 5.6 Spline Smoothing -- 5.7 Multiple Predictors and Additive Models -- 5.8 Comparing Nonparametric Regression Methods -- Background material -- Computational issues -- Exercises -- 6. Smoothing Ordered Categorical Data -- 6.1 Smoothing and Ordered Categorical Data -- 6.2 Smoothing Sparse Multinomials -- 6.3 Smoothing Sparse Contingency Tables -- 6.4 Categorical Data, Regression, and Density Estimation -- Background material -- Computational issues -- Exercises -- 7. Further Applications of Smoothing -- 7.1 Discriminant Analysis -- 7.2 Goodness-of-Fit Tests -- 7.3 Smoothing-Based Parametric Estimation -- 7.4 The Smoothed Bootstrap -- Background material -- Computational issues -- Exercises -- Appendices -- A. Descriptions of the Data Sets -- B. More on Computational Issues -- References -- Author Index