Marginal nonparametric kernel regression accounting for within-subject correlation
AbstractThere has been substantial recent interest in non- and semiparametric methods for longitudinal or clustered data with dependence within clusters. It has been shown rather inexplicably that, when standard kernel smoothing methods are used in a natural way, higher efficiency is obtained by assuming independence than by using the true correlation structure. It is shown here that this result is a natural consequence of how standard kernel methods incorporate the within-subject correlation in the asymptotic setting considered, where the cluster sizes are fixed and the cluster number increases. In this paper, an alternative kernel smoothing method is proposed. Unlike the standard methods, the smallest variance of the new estimator is achieved when the true correlation is assumed. Asymptotically, the variance of the proposed method is uniformly smaller than that of the most efficient working independence approach. A small simulation study shows that significant improvement is obtained for finite samples. Copyright Biometrika Trust 2003, Oxford University Press.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoArticle provided by Biometrika Trust in its journal Biometrika.
Volume (Year): 90 (2003)
Issue (Month): 1 (March)
Contact details of provider:
Postal: Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK
Fax: 01865 267 985
Web page: http://biomet.oxfordjournals.org/
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Yi, Grace Y. & He, Wenqing & Liang, Hua, 2009. "Analysis of correlated binary data under partially linear single-index logistic models," Journal of Multivariate Analysis, Elsevier, vol. 100(2), pages 278-290, February.
- Huggins, Richard, 2004. "A note on nonparametric estimation for clustered data," Statistics & Probability Letters, Elsevier, vol. 69(2), pages 129-133, August.
- Ke Yang, 2013. "An Improved Local-linear Estimator For Nonparametric Regression With Autoregressive Errors," Economics Bulletin, AccessEcon, vol. 33(1), pages 19-27.
- Xu, Peirong & Zhu, Lixing, 2012. "Estimation for a marginal generalized single-index longitudinal model," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 285-299.
- Martins-Filho, Carlos & Yao, Feng, 2009. "Nonparametric regression estimation with general parametric error covariance," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 309-333, March.
- Daniel J. Henderson, 2010. "A test for multimodality of regression derivatives with application to nonparametric growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(3), pages 458-480.
- Qian, Junhui & Wang, Le, 2012.
"Estimating semiparametric panel data models by marginal integration,"
Journal of Econometrics,
Elsevier, vol. 167(2), pages 483-493.
- Qian, Junhui & Wang, Le, 2009. "Estimating Semiparametric Panel Data Models by Marginal Integration," MPRA Paper 18850, University Library of Munich, Germany.
- Henderson, Daniel J. & Carroll, Raymond J. & Li, Qi, 2008. "Nonparametric estimation and testing of fixed effects panel data models," Journal of Econometrics, Elsevier, vol. 144(1), pages 257-275, May.
- Tomasz Gerard Czekaj & Arne Henningsen, 2012. "Comparing Parametric and Nonparametric Regression Methods for Panel Data: the Optimal Size of Polish Crop Farms," IFRO Working Paper 2012/12, University of Copenhagen, Department of Food and Resource Economics.
- Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
- Grace Yi & Wenqing He & Hua Liang, 2011. "Semiparametric marginal and association regression methods for clustered binary data," Annals of the Institute of Statistical Mathematics, Springer, vol. 63(3), pages 511-533, June.
- Li, Jialiang & Xia, Yingcun & Palta, Mari & Shankar, Anoop, 2009. "Impact of unknown covariance structures in semiparametric models for longitudinal data: An application to Wisconsin diabetes data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4186-4197, October.
- Al Kadiri, M. & Carroll, R.J. & Wand, M.P., 2010. "Marginal longitudinal semiparametric regression via penalized splines," Statistics & Probability Letters, Elsevier, vol. 80(15-16), pages 1242-1252, August.
- Xueying Zheng & Wing Fung & Zhongyi Zhu, 2013. "Robust estimation in joint mean–covariance regression model for longitudinal data," Annals of the Institute of Statistical Mathematics, Springer, vol. 65(4), pages 617-638, August.
- You, Jinhong & Zhou, Haibo, 2007. "Two-stage efficient estimation of longitudinal nonparametric additive models," Statistics & Probability Letters, Elsevier, vol. 77(17), pages 1666-1675, November.
- Lena Korber & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers CWP02/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Li, Lexin & Yin, Xiangrong, 2009. "Longitudinal data analysis using sufficient dimension reduction method," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4106-4115, October.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Oxford University Press) or (Christopher F. Baum).
If references are entirely missing, you can add them using this form.