Components and Completion of Partially Observed Functional Data

Citations Over TimeTop 11% of 2014 papers

Abstract

Summary Functional data are traditionally assumed to be observed on the same domain. Motivated by a data set of heart rate temporal profiles, we develop methodology for the analysis of incomplete functional samples where each curve may be observed on a subset of the domain and unobserved elsewhere. We formalize this observation regime and develop the fundamental procedures of functional data analysis for this framework: estimation of parameters (mean and covariance operator) and principal component analysis. Principal scores of a partially observed function cannot be computed directly and we solve this challenging issue by estimating their best predictions as linear functionals of the observed part of the trajectory. Next, we propose a functional completion procedure that recovers the missing part by using the observed part of the curve. We construct prediction intervals for principal scores and bands for missing parts of trajectories. The prediction problems are seen to be ill-posed inverse problems; regularization techniques are used to obtain a stable solution. A simulation study shows the good performance of our methods. We illustrate the methods on the heart rate data and provide practical computational algorithms and theoretical arguments and proofs of all results.

Related Papers

• → Statistical Computing in Functional Data Analysis: TheRPackagefda.usc(2012)363 cited
• → Functional Additive Models(2008)242 cited
• → Limit theorems in functional data analysis with applications(2012)2 cited
• → Functional Data Analysis of Temperature and Precipitation Data(2006)1 cited
• → Derivative Principal Component Analysis for Representing the Time Dynamics of Longitudinal and Functional Data(2017)6 cited