Sieling, Hannes

[["dc.contributor.author","Li, Housen"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Sieling, Hannes"],["dc.contributor.author","Walther, Guenther"],["dc.date.accessioned","2017-09-07T11:50:35Z"],["dc.date.available","2017-09-07T11:50:35Z"],["dc.date.issued","2016"],["dc.description.abstract","The histogram is widely used as a simple, exploratory display of data, but it is usually not clear how to choose the number and size of bins for this purpose. We construct a confidence set of distribution functions that optimally address the two main tasks of the histogram: estimating probabilities and detecting features such as increases and (anti)modes in the distribution. We define the essential histogram as the histogram in the confidence set with the fewest bins. Thus the essential histogram is the simplest visualization of the data that optimally achieves the main tasks of the histogram. We provide a fast algorithm for computing a slightly relaxed version of the essential histogram, which still possesses most of its beneficial theoretical properties, and we illustrate our methodology with examples. An R-package is available online."],["dc.identifier.arxiv","1612.07216"],["dc.identifier.gro","3145900"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3635"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/446"],["dc.language.iso","en"],["dc.notes.intern","lifescience"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.workinggroup","RG Li"],["dc.relation.workinggroup","RG Munk"],["dc.subject","Histogram significant features optimal estimation multiscale testing mode detection"],["dc.title","The Essential Histogram"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","918"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Electronic Journal of Statistics"],["dc.bibliographiccitation.lastpage","959"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Sieling, Hannes"],["dc.date.accessioned","2020-12-10T18:41:47Z"],["dc.date.available","2020-12-10T18:41:47Z"],["dc.date.issued","2016"],["dc.description.abstract","Fast multiple change-point segmentation methods, which additionally provide faithful statistical statements on the number, locations and sizes of the segments, have recently received great attention. In this paper, we propose a multiscale segmentation method, FDRSeg, which controls the false discovery rate (FDR) in the sense that the number of false jumps is bounded linearly by the number of true jumps. In this way, it adapts the detection power to the number of true jumps. We prove a non-asymptotic upper bound for its FDR in a Gaussian setting, which allows to calibrate the only parameter of FDRSeg properly. Moreover, we show that FDRSeg estimates change-point locations, as well as the signal, in a uniform sense at optimal minimax convergence rates up to a log-factor. The latter is w.r.t. L-p-risk, p >= 1, over classes of step functions with bounded jump sizes and either bounded, or even increasing, number of change-points. FDRSeg can be efficiently computed by an accelerated dynamic program; its computational complexity is shown to be linear in the number of observations when there are many change-points. The performance of the proposed method is examined by comparisons with some state of the art methods on both simulated and real datasets. An R-package is available online."],["dc.identifier.doi","10.1214/16-EJS1131"],["dc.identifier.eissn","1935-7524"],["dc.identifier.gro","3141747"],["dc.identifier.isi","000389914600035"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77676"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","DOI-Import GROB-394"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation","RTG 2088: Research Training Group 2088 Discovering structure in complex data: Statistics meets Optimization and Inverse Problems"],["dc.relation.issn","1935-7524"],["dc.title","FDR-control in multiscale change-point segmentation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","347"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Biometrika"],["dc.bibliographiccitation.lastpage","364"],["dc.bibliographiccitation.volume","107"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Sieling, Hannes"],["dc.contributor.author","Walther, Guenther"],["dc.date.accessioned","2021-03-05T08:58:52Z"],["dc.date.available","2021-03-05T08:58:52Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1093/biomet/asz081"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/80277"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/179"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-393"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.eissn","1464-3510"],["dc.relation.issn","0006-3444"],["dc.relation.workinggroup","RG Li"],["dc.relation.workinggroup","RG Munk"],["dc.title","The essential histogram"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]