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Semiparametric theory and missing data

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Cover of 'Semiparametric theory and missing data'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Semiparametric Models
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    Chapter 2 Hilbert Space for Random Vectors
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    Chapter 3 The Geometry of Influence Functions
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    Chapter 4 Semiparametric Models
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    Chapter 5 Other Examples of Semiparametric Models
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    Chapter 6 Models and Methods for Missing Data
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    Chapter 7 Missing and Coarsening at Random for Semiparametric Models
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    Chapter 8 The Nuisance Tangent Space and Its Orthogonal Complement
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    Chapter 9 Augmented Inverse Probability Weighted Complete-Case Estimators
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    Chapter 10 Improving Efficiency and Double Robustness with Coarsened Data
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    Chapter 11 Locally Efficient Estimators for Coarsened-Data Semiparametric Models
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    Chapter 12 Approximate Methods for Gaining Efficiency
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    Chapter 13 Double-Robust Estimator of the Average Causal Treatment Effect
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    Chapter 14 Multiple Imputation: A Frequentist Perspective
Attention for Chapter 11: Locally Efficient Estimators for Coarsened-Data Semiparametric Models
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