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Local additive estimation.

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Local additive estimation. / Park, Juhyun; Seifert, Burkhardt.
In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 72, No. 2, 03.2010, p. 171-191.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Park, J & Seifert, B 2010, 'Local additive estimation.', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 72, no. 2, pp. 171-191. https://doi.org/10.1111/j.1467-9868.2009.00731.x

APA

Park, J., & Seifert, B. (2010). Local additive estimation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(2), 171-191. https://doi.org/10.1111/j.1467-9868.2009.00731.x

Vancouver

Park J, Seifert B. Local additive estimation. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2010 Mar;72(2):171-191. doi: 10.1111/j.1467-9868.2009.00731.x

Author

Park, Juhyun ; Seifert, Burkhardt. / Local additive estimation. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2010 ; Vol. 72, No. 2. pp. 171-191.

Bibtex

@article{af5a72eb42a448579629af5787fbd080,
title = "Local additive estimation.",
abstract = "Additive models are popular in high dimensional regression problems owing to their flexibility in model building and optimality in additive function estimation. Moreover, they do not suffer from the so-called curse of dimensionality generally arising in non-parametric regression settings. Less known is the model bias that is incurred from the restriction to the additive class of models. We introduce a new class of estimators that reduces additive model bias, yet preserves some stability of the additive estimator. The new estimator is constructed by localizing the additivity assumption and thus is named the local additive estimator. It follows the spirit of local linear estimation but is shown to be able to relieve partially the dimensionality problem. Implementation can be easily made with any standard software for additive regression. For detailed analysis we explicitly use the smooth backfitting estimator of Mammen, Linton and Nielsen.",
keywords = "Additive models • Backfitting • Local polynomial smoothing • Non-parametric regression",
author = "Juhyun Park and Burkhardt Seifert",
year = "2010",
month = mar,
doi = "10.1111/j.1467-9868.2009.00731.x",
language = "English",
volume = "72",
pages = "171--191",
journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Local additive estimation.

AU - Park, Juhyun

AU - Seifert, Burkhardt

PY - 2010/3

Y1 - 2010/3

N2 - Additive models are popular in high dimensional regression problems owing to their flexibility in model building and optimality in additive function estimation. Moreover, they do not suffer from the so-called curse of dimensionality generally arising in non-parametric regression settings. Less known is the model bias that is incurred from the restriction to the additive class of models. We introduce a new class of estimators that reduces additive model bias, yet preserves some stability of the additive estimator. The new estimator is constructed by localizing the additivity assumption and thus is named the local additive estimator. It follows the spirit of local linear estimation but is shown to be able to relieve partially the dimensionality problem. Implementation can be easily made with any standard software for additive regression. For detailed analysis we explicitly use the smooth backfitting estimator of Mammen, Linton and Nielsen.

AB - Additive models are popular in high dimensional regression problems owing to their flexibility in model building and optimality in additive function estimation. Moreover, they do not suffer from the so-called curse of dimensionality generally arising in non-parametric regression settings. Less known is the model bias that is incurred from the restriction to the additive class of models. We introduce a new class of estimators that reduces additive model bias, yet preserves some stability of the additive estimator. The new estimator is constructed by localizing the additivity assumption and thus is named the local additive estimator. It follows the spirit of local linear estimation but is shown to be able to relieve partially the dimensionality problem. Implementation can be easily made with any standard software for additive regression. For detailed analysis we explicitly use the smooth backfitting estimator of Mammen, Linton and Nielsen.

KW - Additive models • Backfitting • Local polynomial smoothing • Non-parametric regression

U2 - 10.1111/j.1467-9868.2009.00731.x

DO - 10.1111/j.1467-9868.2009.00731.x

M3 - Journal article

VL - 72

SP - 171

EP - 191

JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

SN - 1369-7412

IS - 2

ER -