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

TL;DR

This paper introduces kernel-based nonparametric estimators for the mean of functional data in survey sampling, addressing missing data issues and variance estimation, with applications to electricity load curve analysis.

Contribution

It extends kernel smoothing techniques to handle missing trajectories in survey sampling, providing new estimators and variance estimation methods for functional data.

Findings

Proposes three new estimators accounting for missing data.

Develops pointwise variance estimators using linearization.

Analyzes stratified sampling case in detail.

Abstract

In the near future, millions of load curves measuring the electricity consumption of French households in small time grids (probably half hours) will be available. All these collected load curves represent a huge amount of information which could be exploited using survey sampling techniques. In particular, the total consumption of a specific cus- tomer group (for example all the customers of an electricity supplier) could be estimated using unequal probability random sampling methods. Unfortunately, data collection may undergo technical problems resulting in missing values. In this paper we study a new estimation method for the mean curve in the presence of missing values which consists in extending kernel estimation techniques developed for longitudinal data analysis to sampled curves. Three nonparametric estimators that take account of the missing pieces of trajectories are suggested. We also study pointwise variance estimators which are based on linearization techniques. The particular but very important case of stratified sampling is then specifically studied. Finally, we discuss some more practical aspects such as choosing the bandwidth values for the kernel and estimating the probabilities of observation of the trajectories.