Estimating a common period for a set of irregularly sampled functions with applications to periodic variable star data

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dc.citation.firstpage165en_US
dc.citation.issueNumber1en_US
dc.citation.journalTitleThe Annals of Applied Statisticsen_US
dc.citation.lastpage197en_US
dc.citation.volumeNumber10en_US
dc.contributor.authorLong, James P.en_US
dc.contributor.authorChi, Eric C.en_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.date.accessioned2017-05-09T20:16:07Zen_US
dc.date.available2017-05-09T20:16:07Zen_US
dc.date.issued2016en_US
dc.description.abstractWe consider the problem of estimating a common period for a set of functions sampled at irregular intervals. The motivating problem arises in astronomy, where the functions represent a star’s observed brightness over time through different photometric filters. While current methods perform well when the brightness is sampled densely enough in at least one filter, they break down when no brightness function is densely sampled. In this paper we introduce two new methods for period estimation in this important latter case. The first, multiband generalized Lomb–Scargle (MGLS), extends the frequently used Lomb–Scargle method to naïvely combine information across filters. The second, penalized generalized Lomb–Scargle (PGLS), builds on MGLS by more intelligently borrowing strength across filters. Specifically, we incorporate constraints on the phases and amplitudes across the different functions using a nonconvex penalized likelihood function. We develop a fast algorithm to optimize the penalized likelihood that combines block coordinate descent with the majorization–minimization (MM) principle. We test and validate our methods on synthetic and real astronomy data. Both PGLS and MGLS improve period estimation accuracy over current methods based on using a single function; moreover, PGLS outperforms MGLS and other leading methods when the functions are sparsely sampled.en_US
dc.identifier.citationLong, James P., Chi, Eric C. and Baraniuk, Richard G.. "Estimating a common period for a set of irregularly sampled functions with applications to periodic variable star data." <i>The Annals of Applied Statistics,</i> 10, no. 1 (2016) Project Euclid: 165-197. https://doi.org/10.1214/15-AOAS885.en_US
dc.identifier.doihttps://doi.org/10.1214/15-AOAS885en_US
dc.identifier.urihttps://hdl.handle.net/1911/94215en_US
dc.language.isoengen_US
dc.publisherProject Eucliden_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.subject.keywordastrostatisticsen_US
dc.subject.keywordpenalized likelihooden_US
dc.subject.keywordperiod estimationen_US
dc.subject.keywordfunctional dataen_US
dc.subject.keywordMM algorithmen_US
dc.subject.keywordblock coordinate descenten_US
dc.titleEstimating a common period for a set of irregularly sampled functions with applications to periodic variable star dataen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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