Smooth backfitting in practice

doi:10.1111/j.1467-9868.2005.00487.x

Smooth backfitting in practice

2004-12-13

66

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AI

DOI：10.1111/j.1467-9868.2005.00487.x

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Compared with the classical backfitting of Buja, Hastie and Tibshirani, the smooth backfitting estimator (SBE) of Mammen, Linton and Nielsen not only provides complete asymptotic theory under weaker conditions but is also more efficient, robust and easier to calculate. However, the original paper describing the SBE method is complex and the practical as well as the theoretical advantages of the method have still neither been recognized nor accepted by the statistical community. We focus on a clear presentation of the idea, the main theoretical results and practical aspects like implementation and simplification of the algorithm. We introduce a feasible cross-validation procedure and apply it to the problem of data-driven bandwidth choice for the SBE. By simulations it is shown that the SBE and our cross-validation work very well indeed. In particular, the SBE is less affected by sparseness of data in high dimensional regression problems or strongly correlated designs. The SBE has reasonable performance even in 100-dimensional additive regression problems.

Keyword:

backfitting

bandwidth choice

computational statistics

cross-validation

nonparametric estimation

Smooth backfitting in practice

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