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) 5 Some theory for additive models: Section 5.1: Introduction.
[105] Section 5.2: Estimating equations for additive models. [106]
Section 5.2.1: $L_2$ function spaces. [107] [108] [109]
Section 5.2.2: Penalized least-squares. [110] [111]
Section 5.2.3: Reproducing-kernel Hilbert-spaces. [112] [113]
Section 5.3: Solutions to the estimating equations.
Section 5.3.1: Introduction. [114] Section 5.3.2: Projection smoothers.
[115]
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 [116] Section 5.3.3: Semi-parametric models. [117] Section 5.3.4: Backfitting 
with two smoothers. [118] [119]
Section 5.3.5: Existence and uniqueness: $p$-smoothers. [120]
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 [121]
Section 5.3.6: Convergence of backfitting: $p$-smoothers.
Section 5.3.7: Summary of the main results of the section. [122] Section 5.4: S
pecial topics. Section 5.4.1: Weighted additive models. [123] Section 5.4.2: A 
modified backfitting algorithm. [124] [125]
Section 5.4.3: Explicit solutions to the estimating equations. Section 5.4.4: S
tandard errors. [126]
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l.1211  The standard-error bands in Fig.~\fone
                                              \ of Chapter~4 are constructed
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 [127] Section 5.4.5: Degrees of freedom. [128] Section 5.4.6: A Bayesian versi
on of additive models. [129] Section 5.5: Bibliographic notes. [130] Section 5.
6: Further results and exercises 5. [131] [132] [133] [134] [135]
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