Sparse estimation in ordinary kriging for functional data

TL;DR

This paper proposes a sparse estimation method for functional data in ordinary kriging, utilizing lasso regularization to identify essential locations for prediction, with demonstrated effectiveness through simulations and real data.

Contribution

It introduces a novel sparse estimation approach in functional kriging using lasso regularization and an augmented Lagrange algorithm for efficient computation.

Findings

The method effectively shrinks some weights to zero, identifying necessary locations.

Simulation results show accurate prediction and variable selection.

Real data analysis confirms practical applicability.

Abstract

We introduce a sparse estimation in the ordinary kriging for functional data. The functional kriging predicts a feature given as a function at a location where the data are not observed by a linear combination of data observed at other locations. To estimate the weights of the linear combination, we apply the lasso-type regularization in minimizing the expected squared error. We derive an algorithm to derive the estimator using the augmented Lagrange method. Tuning parameters included in the estimation procedure are selected by cross-validation. Since the proposed method can shrink some of the weights of the linear combination toward zeros exactly, we can investigate which locations are necessary or unnecessary to predict the feature. Simulation and real data analysis show that the proposed method appropriately provides reasonable results.