Robust functional principal component analysis for non-Gaussian longitudinal data

Functional Principal Component Analysis for Longitudinal and Survival Data

This paper proposes a nonparametric approach for jointly modelling longitudinal and survival data using functional principal components. The proposed model is data-adaptive in the sense that it does not require pre-specified functional forms for longitudinal trajectories and it automatically detects characteristic patterns. The longitudinal trajectories observed with measurement error are repre...

Principal Component Analysis on non-Gaussian Dependent Data

In this paper, we analyze the performance of a semiparametric principal component analysis named Copula Component Analysis (COCA) (Han & Liu, 2012) when the data are dependent. The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. We study the scenario where the observations are drawn from non-i.i.d. processes ...

Preliminary-summation-based principal component analysis for non-Gaussian processes

Functional principal component analysis of fMRI data.

We describe a principal component analysis (PCA) method for functional magnetic resonance imaging (fMRI) data based on functional data analysis, an advanced nonparametric approach. The data delivered by the fMRI scans are viewed as continuous functions of time sampled at the interscan interval and subject to observational noise, and are used accordingly to estimate an image in which smooth func...

Gaussian Processes for Principal Component Analysis

We show how the supervised method of Gaussian Processes may be used for Principal Component Analysis using two intuitions about the nature of the first principal component filters.