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Volume 38, Issue 1
Model-Assisted Estimators with Auxiliary Functional Data

Chao Liu, Huiming Zhang & Jing Yan

Commun. Math. Res., 38 (2022), pp. 81-98.

Published online: 2021-11

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  • Abstract

Few studies focus on the application of functional data to the field of design-based survey sampling. In this paper, the scalar-on-function regression model-assisted method is proposed to estimate the finite population means with auxiliary functional data information. The functional principal component method is used for the estimation of functional linear regression model. Our proposed functional linear regression model-assisted (FLR-assisted) estimator is asymptotically design-unbiased, consistent under mild conditions. Simulation experiments and real data analysis show that the FLR-assisted estimators are more efficient than the Horvitz-Thompson estimators under different sampling designs.

  • Keywords

Survey sampling, semi-supervised inference, model-assisted estimator, Horvitz-Thompson estimator, functional linear regression.

  • AMS Subject Headings

62K25, 62D05

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CMR-38-81, author = {Chao and Liu and and 21338 and and Chao Liu and Huiming and Zhang and and 21339 and and Huiming Zhang and Jing and Yan and and 21340 and and Jing Yan}, title = {Model-Assisted Estimators with Auxiliary Functional Data}, journal = {Communications in Mathematical Research }, year = {2021}, volume = {38}, number = {1}, pages = {81--98}, abstract = {

Few studies focus on the application of functional data to the field of design-based survey sampling. In this paper, the scalar-on-function regression model-assisted method is proposed to estimate the finite population means with auxiliary functional data information. The functional principal component method is used for the estimation of functional linear regression model. Our proposed functional linear regression model-assisted (FLR-assisted) estimator is asymptotically design-unbiased, consistent under mild conditions. Simulation experiments and real data analysis show that the FLR-assisted estimators are more efficient than the Horvitz-Thompson estimators under different sampling designs.

}, issn = {2707-8523}, doi = {https://doi.org/10.4208/cmr.2021-0056}, url = {http://global-sci.org/intro/article_detail/cmr/19958.html} }
TY - JOUR T1 - Model-Assisted Estimators with Auxiliary Functional Data AU - Liu , Chao AU - Zhang , Huiming AU - Yan , Jing JO - Communications in Mathematical Research VL - 1 SP - 81 EP - 98 PY - 2021 DA - 2021/11 SN - 38 DO - http://doi.org/10.4208/cmr.2021-0056 UR - https://global-sci.org/intro/article_detail/cmr/19958.html KW - Survey sampling, semi-supervised inference, model-assisted estimator, Horvitz-Thompson estimator, functional linear regression. AB -

Few studies focus on the application of functional data to the field of design-based survey sampling. In this paper, the scalar-on-function regression model-assisted method is proposed to estimate the finite population means with auxiliary functional data information. The functional principal component method is used for the estimation of functional linear regression model. Our proposed functional linear regression model-assisted (FLR-assisted) estimator is asymptotically design-unbiased, consistent under mild conditions. Simulation experiments and real data analysis show that the FLR-assisted estimators are more efficient than the Horvitz-Thompson estimators under different sampling designs.

Chao Liu, Huiming Zhang & Jing Yan. (2021). Model-Assisted Estimators with Auxiliary Functional Data. Communications in Mathematical Research . 38 (1). 81-98. doi:10.4208/cmr.2021-0056
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