Nonparametric Smoothing and Lack-of-Fit Tests

1. Front Matter

   Pages i-xii

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2. Introduction

   • Jeffrey D. Hart

   Pages 1-3

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3. Some Basic Ideas of Smoothing

   • Jeffrey D. Hart

   Pages 4-49

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4. Statistical Properties of Smoothers

   • Jeffrey D. Hart

   Pages 50-83

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5. Data-Driven Choice of Smoothing Parameters

   • Jeffrey D. Hart

   Pages 84-116

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6. Classical Lack-of-Fit Tests

   • Jeffrey D. Hart

   Pages 117-143

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7. Lack-of-Fit Tests Based on Linear Smoothers

   • Jeffrey D. Hart

   Pages 144-163

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8. Testing for Association via Automated Order Selection

   • Jeffrey D. Hart

   Pages 164-207

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9. Data-Driven Lack-of-Fit Tests for General Parametric Models

   • Jeffrey D. Hart

   Pages 208-225

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10. Extending the Scope of Application

    • Jeffrey D. Hart

    Pages 226-252

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11. Some Examples

    • Jeffrey D. Hart

    Pages 253-266

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12. Back Matter

    Pages 267-289

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The The primary primary aim aim of of this this book book is is to to explore explore the the use use of of nonparametric nonparametric regres­ regres­ sion sion (i. e. , (i. e. , smoothing) smoothing) methodology methodology in in testing testing the the fit fit of of parametric parametric regression regression models. models. It It is is anticipated anticipated that that the the book book will will be be of of interest interest to to an an audience audience of of graduate graduate students, students, researchers researchers and and practitioners practitioners who who study study or or use use smooth­ smooth­ ing ing methodology. methodology. Chapters Chapters 2-4 2-4 serve serve as as a a general general introduction introduction to to smoothing smoothing in in the the case case of of a a single single design design variable. variable. The The emphasis emphasis in in these these chapters chapters is is on on estimation estimation of of regression regression curves, curves, with with hardly hardly any any mention mention of of the the lack-of­ lack-of­ fit fit problem. problem. As As such, such, Chapters Chapters 2-4 2-4 could could be be used used as as the the foundation foundation of of a a graduate graduate level level statistics statistics course course on on nonparametric nonparametric regression. regression.

• Calc
• Mathematica
• Scope
• calculus
• design
• distribution
• eXist
• kernel
• knowledge
• mathematical statistics
• model
• review
• selection
• statistics
• testing

• Department of Statistics, Texas A&M University, College Station, USA

  Jeffrey D. Hart

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