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dc.contributor.authorLarriba González, Yolanda 
dc.date.accessioned2017-09-27T08:26:24Z
dc.date.available2017-09-27T08:26:24Z
dc.date.issued2017
dc.identifier.citationAdvances in Directional Statistics (ADISTA 2017). Roma Tre University, Rome, Italy, p.46es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/25947
dc.description.abstractMany biological processes, such as cell cycle, circadian clock or menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit periodic patterns over time. Modelling these rhythms is a challenge in literature since usually the sampling density is low, the number of periods is generally two and the underlying signals adopt a wide range of temporal patterns, see Larriba et al. (2016). Several authors proposed parametric functions of time, such as the sinusoidal function, to model these signals. However these parametric functions might be too rigid for data derived from cell-cycle or circadian clock. Among these, a common shape of interest to a biologist is the circular up-down-up signal with a unique peak (U) and a unique trough (L) within each period. The shape of these signals is entirely described by mathematical inequalities among their components which allow to establish a relationship between the euclidean and the circular space using circular isotonic regression. In this work we state this connection between the euclidean and the circular space based on circular isotonic regression, formulate the ML isotonic regression estimator under circular up-down-up constraints and assess its computational advantages to calculate the circular isotonic regression estimator (CIRE), see Rueda et al. (2009). Results are shown both on simulations and on real data.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleModelling biological rhythms using order restricted inferencees
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.title.eventAdvances in Directional Statistics (ADISTA 2017)es
dc.description.projectMINECO grant MTM2015-71217-Res
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International


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