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1.1     ! root        1: \first{5} {Some theory for additive models } {105}
        !             2: \second{5.1}{Introduction}{105}
        !             3: \second{5.2}{Estimating equations for additive models}{106}
        !             4: \third{5.2.1}{$L_2$ function spaces}{107}
        !             5: \third{5.2.2}{Penalized least-squares}{110}
        !             6: \third{5.2.3}{Reproducing-kernel Hilbert-spaces}{112}
        !             7: \second{5.3}{Solutions to the estimating equations}{114}
        !             8: \third{5.3.1}{Introduction}{114}
        !             9: \third{5.3.2}{Projection smoothers}{115}
        !            10: \third{5.3.3}{Semi-parametric models}{117}
        !            11: \third{5.3.4}{Backfitting with two smoothers}{118}
        !            12: \third{5.3.5}{Existence and uniqueness: $p$-smoothers}{120}
        !            13: \third{5.3.6}{Convergence of backfitting: $p$-smoothers}{122}
        !            14: \third{5.3.7}{Summary of the main results of the section}{122}
        !            15: \second{5.4}{Special topics}{123}
        !            16: \third{5.4.1}{Weighted additive models}{123}
        !            17: \third{5.4.2}{A modified backfitting algorithm}{124}
        !            18: \third{5.4.3}{Explicit solutions to the estimating equations}{126}
        !            19: \third{5.4.4}{Standard errors}{126}
        !            20: \third{5.4.5}{Degrees of freedom}{128}
        !            21: \third{5.4.6}{A Bayesian version of additive models}{129}
        !            22: \second{5.5}{Bibliographic notes}{130}
        !            23: \second{5.6}{Further results and exercises 5}{131}

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