Estimating with kernel smoothers the mean of functional data in a finite population setting. A note on variance estimation in presence of partially observed trajectories

DOI10.1016/j.spl.2015.01.025zbMath1396.62005arXiv1410.4105OpenAlexW2963030688MaRDI QIDQ2344883

Anne De Moliner, Hervé Cardot, Camelia Goga

Publication date: 18 May 2015

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1410.4105

zbMATH Keywords

survey samplingnonparametric estimationfunctional dataHorvitz-Thompson estimatormissing valuesHájek estimator

Mathematics Subject Classification ID

Density estimation (62G07) Sampling theory, sample surveys (62D05)

Cites Work

• Model assisted survey sampling
• Nonparametric smoothing and lack-of-fit tests
• Uniform convergence and asymptotic confidence bands for model-assisted estimators of the mean of sampled functional data
• Consistency of cross-validation when the data are curves
• Confidence bands for Horvitz-Thompson estimators using sampled noisy functional data
• Properties of principal component methods for functional and longitudinal data analysis
• Horvitz-Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling
• Kernel Regression Estimation Using Repeated Measurements Data
• Nonparametric Regression Analysis of Longitudinal Data
• Estimation in Surveys with Nonresponse
• A Generalization of Sampling Without Replacement From a Finite Universe
• Sampling Methods

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