2008/12/31 by Li Wang, Lily Wang · 14 citations
Computer Science · Mathematics · #Applied mathematics #Bayesian Methods and Mixture Models #Estimator #Mathematical optimization #Mathematics #Multivariate statistics #Semiparametric regression #Single-index model #Spline (mechanical) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics #math.ST #stat.ME #stat.TH
paper · pdf · doi:10.1080/10485250902773849
published in Journal of nonparametric statistics 21(4), 487-504 (Taylor & Francis) · 30 pages
arxiv created 2008/12/31 · openalex publication_date 2009/03/19 · arxiv updated 2019/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
A model-assisted semiparametric method of estimating finite-population totals is investigated to improve the precision of survey estimators by incorporating multivariate auxiliary information. The proposed superpopulation model is a single-index model (SIM) which has proven to be a simple and efficient semiparametric tool in multivariate regression. A class of estimators based on polynomial spline regression is proposed. These estimators are robust against deviation from SIMs. Under standard design conditions, the proposed estimators are asymptotically design-unbiased, consistent and asymptotically normal. An iterative optimisation routine is provided that is sufficiently fast for users to analyze large and complex survey data within seconds. The proposed method has been applied to simulated datasets and MU281 dataset, which have provided strong evidence that corroborates with the asymptotic theory.