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Annotation of researchv10dc/vol2/index/contents.tex, revision 1.1.1.1

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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