Curve alignment by moments

Simple item page
dc.creatorJames, Gareth M.
dc.date2007-12-10
dc.date.accessioned2026-07-07T08:49:28Z
dc.date.available2026-07-07T08:49:28Z
dc.descriptionA significant problem with most functional data analyses is that of misaligned curves. Without adjustment, even an analysis as simple as estimation of the mean will fail. One common method to synchronize a set of curves involves equating ``landmarks'' such as peaks or troughs. The landmarks method can work well but will fail if marker events can not be identified or are missing from some curves. An alternative approach, the ``continuous monotone registration'' method, works by transforming the curves so that they are as close as possible to a target function. This method can also perform well but is highly dependent on identifying an accurate target function. We develop an alignment method based on equating the ``moments'' of a given set of curves. These moments are intended to capture the locations of important features which may represent local behavior, such as maximums and minimums, or more global characteristics, such as the slope of the curve averaged over time. Our method works by equating the moments of the curves while also shrinking toward a common shape. This allows us to capture the advantages of both the landmark and continuous monotone registration approaches. The method is illustrated on several data sets and a simulation study is performed.
dc.descriptionPublished in at http://dx.doi.org/10.1214/07-AOAS127 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0712.1425
dc.identifierhttp://arxiv.org/abs/0712.1425
dc.identifierAnnals of Applied Statistics 2007, Vol. 1, No. 2, 480-501
dc.identifierdoi:10.1214/07-AOAS127
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/144309
dc.subjectApplications
dc.titleCurve alignment by moments
dc.typetext

Files

Collections

arXiv