Imputed Estimation For Varying Coefficient Models with Missing Covariates

Pei Xin Zhao

doi:10.4028/www.scientific.net/AMR.787.1089

Paper Titles

Optimal Locating of Misbehavior Node in Wireless Sensor Network
p.1062

A Model of Three Points for Location Estimation in Wireless Sensor Networks
p.1066

Determinants for Product and Process Innovation: A Review in the Perspective of TQM Practices
p.1072

Extended Mapping Approach and Exact Solutions for a (1+1)-Dimensional Benjamin-Boma-Mahony Equation
p.1077

A Numerical Framework for Free Surface Flow and Wettability Based on Arbitrary Lagrange-Eulerian Method
p.1080

Bending Response of Exponentially Graded Plates by a Meshless Method
p.1085

Imputed Estimation For Varying Coefficient Models with Missing Covariates
p.1089

Attractor and Invariance of a Class of Chaotic Dynamic Systems
p.1093

Training Sequence Design Based on Channel Estimation
p.1097

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 787Imputed Estimation For Varying Coefficient Models...

Imputed Estimation For Varying Coefficient Models with Missing Covariates

Abstract:

By using the imputation-based estimating equation method, an imputed estimation procedure for the coefficient functions is proposed. The proposed procedure can attenuate the effect of the missing data, and performs well for the finite sample.

You might also be interested in these eBooks

Advanced Materials Researches, Engineering and Manufacturing Technologies in Industry

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 787)

Pages:

1089-1092

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.787.1089

Citation:

Cite this paper

Online since:

September 2013

Authors:

Pei Xin Zhao

Keywords:

Imputed Estimation, Missing Covariate, Varying Coefficient Model

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Q.H. Wang and J.N.K. Rao: Empirical likelihood for linear models under imputation for missing responses. Canad. J. Statist. Vol. 29, pp.597-608 (2001).

DOI: 10.2307/3316009

Google Scholar

[2] L.G. Xue: Empirical likelihood for linear models with missing responses. J. Multivariate Anal. Vol. 100, pp.1353-1366 (2009).

DOI: 10.1016/j.jmva.2008.12.009

Google Scholar

[3] H. Liang: Generalized partially linear models with missing covariates. Journal of Multivariate Analysis. Vol. 99, pp.880-895 (2008).

DOI: 10.1016/j.jmva.2007.05.004

Google Scholar

[4] Q.H. Wang: Statistical estimation in partial linear models with covariate data missing at random. Annals of the Institute of Statistical Mathematics. Vol. 61, pp.47-84 (2009).

DOI: 10.1007/s10463-007-0137-1

Google Scholar

[5] H. Liang, S.J. Wang, J.M. Robbins and R.J. Carroll: Estimation in partially linear models with missing covariates. Journal of the American Statistical Association. Vol. 99, pp.357-367 (2004).

DOI: 10.1198/016214504000000421

Google Scholar

[6] H. Wong, S.J. Guo, M. Chen and W.C. IP: On locally weighted estimation and hypothesis testing of varying coefficient models with missing covariates. Journal of Statistical Planning and Inference. Vol. 139, pp.2933-2951 (2009).

DOI: 10.1016/j.jspi.2009.01.016

Google Scholar

[7] P.X. Zhao and L.G. Xue: Variable selection for semiparametric varying coefficient partially linear models with missing response at random. Acta Mathematica Sinica, English Series. Vol. 27, pp.2205-2216 (2011).

DOI: 10.1007/s10114-011-9200-1

Google Scholar