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| Preface | |
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| Nonparametric Kernel Methods | |
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| Density Estimation | |
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| Univariate Density Estimation | |
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| Univariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods | |
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| Univariate Bandwidth Selection: Cross-Validation ZMethods | |
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| Least Squares Cross-Validation | |
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| Likelihood Cross-Validation | |
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| An Illustration of Data-Driven Bandwidth Selection | |
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| Univariate CDF Estimation | |
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| Univariate CDF Bandwidth Selection: Cross- Validation Methods | |
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| Multivariate Density Estimation | |
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| Multivariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods | |
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| Multivariate Bandwidth Selection: Cross-Validation Methods | |
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| Least Squares Cross-Validation | |
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| Likelihood Cross-Validation | |
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| Asymptotic Normality of Density Estimators | |
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| Uniform Rates of Convergence | |
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| Higher Order Kernel Functions | |
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| Proof of Theorem 1.4 (Uniform Almost Sure Convergence) | |
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| Applications | |
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| Female Wage Inequality | |
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| Unemployment Rates and City Size | |
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| Adolescent Growth | |
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| Old Faithful Geyser Data | |
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| Evolution of Real Income Distribution in Italy, 1951-1998 | |
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| Exercises | |
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| Regression | |
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| Local Constant Kernel Estimation | |
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| Intuition Underlying the Local Constant Kernel Estimator | |
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| Local Constant Bandwidth Selection | |
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| Rule-of-Thumb and Plug-In Methods | |
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| Least Squares Cross-Validation | |
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| AICc | |
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| The Presence of Irrelevant Regressors | |
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| Some Further Results on Cross-Validation | |
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| Uniform Rates of Convergence | |
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| Local Linear Kernel Estimation | |
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| Local Linear Bandwidth Selection: Least Squares Cross-Validation | |
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| Local Polynomial Regression (General pth Order) | |
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| The Univariate Case | |
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| The Multivariate Case | |
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| Asymptotic Normality of Local Polynomial Estimators | |
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| Applications | |
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| Prestige Data | |
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| Adolescent Growth | |
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| Inflation Forecasting and Money Growth | |
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| Proofs | |
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| Derivation of (2.24) | |
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| Proof of Theorem 2.7 | |
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| Definitions of Al,p+1 and Vl Used in Theorem 2.10 | |
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| Exercises | |
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| Frequency Estimation with Mixed Data | |
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| Probability Function Estimation with Discrete Data | |
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| Regression with Discrete Regressors | |
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| Estimation with Mixed Data: The Frequency Approach | |
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| Density Estimation with Mixed Data | |
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| Regression with Mixed Data | |
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| Some Cautionary Remarks on Frequency Methods | |
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| Proofs | |
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| Proof of Theorem 3.1 | |
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| Exercises | |
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| Kernel Estimation with Mixed Data | |
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| Smooth Estimation of Joint Distributions with Discrete Data | |
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| Smooth Regression with Discrete Data | |
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| Kernel Regression with Discrete Regressors: The Irrelevant Regressor Case | |
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| Regression with Mixed Data: Relevant Regressors | |
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| Smooth Estimation with Mixed Data | |
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| The Cross-Validation Method | |
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| Regression with Mixed Data: Irrelevant Regressors | |
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| Ordered Discrete Variables | |
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| Applications | |
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| Food-Away-from-Home Expenditure | |
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| Modeling Strike Volume | |
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| Exercises | |
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| Conditional Density Estimation | |
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| Conditional Density Estimation: Relevant Variables | |
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| Conditional Density Bandwidth Selection | |
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| Least Squares Cross-Validation: Relevant Variables | |
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| Maximum Likelihood Cross-Validation: Relevant Variables | |
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| Conditional Density Estimation: Irrelevant Variables | |
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| The Multivariate Dependent Variables Case | |
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| The General Categorical Data Case | |
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| Proof of Theorem 5.5 | |
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| Applications | |
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| A Nonparametric Analys | |