Local principal differential analysis: Graphical methods for functional data with covariates. Issue 3 (16th March 2017)

• Record Type: Journal Article
• Authors: Staniswalis, Joan G.; Dodoo, Christopher; Sharma, Anu
• Is Part Of: Communications in statistics. Volume 46:Issue 3(2017)
• Page Start: 2346
• Page End: 2359
• Publication Date: 2017-03-16
• Subjects: CAEP curves -- Differential operator -- Low-dimensional approximation.; 62G08; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2015.1043387
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: We focus on principal differential analysis (PDA) of functional data for obtaining a low-dimensional representation of a collection of curves. PDA assumes there exists a linear differential operator that results in the zero-function when it is applied to each of the data curves, or equivalently, that the curves belong to a low-dimensional subspace of a normed linear space. PDA sets out to estimate this linear differential operator from the data and proceeds from there. Our contribution is to explain how subject covariates can be incorporated into a PDA analysis for graphical exploration of patterns in the data.