Functional Principal Component Analysis for Functional Data with Informative Dropout

Music City Center 

Analysis of sparse functional data has been primarily conducted under the assumption of an uninformative sampling design. We consider the case where the data follow a visit process that induces dropout dependent on the functional outcomes. Such a case leads to bias in model component estimation when standard functional data analysis techniques are used. Recent research has presented methods to adjust estimation for informative observation times and censoring, though not while also considering dropout informed by prior functional outcomes. We propose a joint visit process and functional data model following that accounts for both informative observation times and dropout while allowing for sparse, irregular observation times. The performance of the proposed method is shown in numerical studies.

Functional Data Analysis

Truncated Data

Informative sampling design

Pseudo-Likelihood Methods

Nonparametric statistics

Sparse Functional Principal Component Analysis 

Section on Nonparametric Statistics