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","495"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society: Series B (Statistical Methodology)"],["dc.bibliographiccitation.lastpage","580"],["dc.bibliographiccitation.volume","76"],["dc.contributor.author","Frick, Klaus"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Sieling, Hannes"],["dc.date.accessioned","2017-09-07T11:46:13Z"],["dc.date.available","2017-09-07T11:46:13Z"],["dc.date.issued","2014"],["dc.description.abstract","We introduce a new estimator, the simultaneous multiscale change point estimator SMUCE, for the change point problem in exponential family regression. An unknown step function is estimated by minimizing the number of change points over the acceptance region of a multiscale test at a level alpha. The probability of overestimating the true number of change points K is controlled by the asymptotic null distribution of the multiscale test statistic. Further, we derive exponential bounds for the probability of underestimating K. By balancing these quantities, alpha will be chosen such that the probability of correctly estimating K is maximized. All results are even non-asymptotic for the normal case. On the basis of these bounds, we construct (asymptotically) honest confidence sets for the unknown step function and its change points. At the same time, we obtain exponential bounds for estimating the change point locations which for example yield the minimax rate O(n-1) up to a log-term. Finally, the simultaneous multiscale change point estimator achieves the optimal detection rate of vanishing signals as n ->infinity, even for an unbounded number of change points. We illustrate how dynamic programming techniques can be employed for efficient computation of estimators and confidence regions. The performance of the multiscale approach proposed is illustrated by simulations and in two cutting edge applications from genetic engineering and photoemission spectroscopy."],["dc.identifier.doi","10.1111/rssb.12047"],["dc.identifier.gro","3142116"],["dc.identifier.isi","000335755900002"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4722"],["dc.language.iso","en"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.eissn","1467-9868"],["dc.relation.issn","1369-7412"],["dc.title","Multiscale change point inference"],["dc.type","review"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2255"],["dc.bibliographiccitation.issue","16"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","2262"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Futschik, Andreas"],["dc.contributor.author","Hotz, Thomas"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Sieling, Hannes"],["dc.date.accessioned","2017-09-07T11:45:36Z"],["dc.date.available","2017-09-07T11:45:36Z"],["dc.date.issued","2014"],["dc.description.abstract","Motivation: DNA segmentation, i.e. the partitioning of DNA in compositionally homogeneous segments, is a basic task in bioinformatics. Different algorithms have been proposed for various partitioning criteria such as Guanine/Cytosine (GC) content, local ancestry in population genetics or copy number variation. A critical component of any such method is the choice of an appropriate number of segments. Some methods use model selection criteria and do not provide a suitable error control. Other methods that are based on simulating a statistic under a null model provide suitable error control only if the correct null model is chosen. Results: Here, we focus on partitioning with respect to GC content and propose a new approach that provides statistical error control: as in statistical hypothesis testing, it guarantees with a user-specified probability 1 - alpha that the number of identified segments does not exceed the number of actually present segments. The method is based on a statistical multiscale criterion, rendering this as a segmentation method that searches segments of any length (on all scales) simultaneously. It is also accurate in localizing segments: under benchmark scenarios, our approach leads to a segmentation that is more accurate than the approaches discussed in the comparative review of Elhaik et al. In our real data examples, we find segments that often correspond well to features taken from standard University of California at Santa Cruz (UCSC) genome annotation tracks."],["dc.identifier.doi","10.1093/bioinformatics/btu180"],["dc.identifier.gro","3142071"],["dc.identifier.isi","000342746000003"],["dc.identifier.pmid","24753487"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4222"],["dc.language.iso","en"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.eissn","1460-2059"],["dc.relation.issn","1367-4803"],["dc.title","Multiscale DNA partitioning: statistical evidence for segments"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Foundations of Computational Mathematics"],["dc.bibliographiccitation.lastpage","33"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Bauer, Ulrich"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Sieling, Hannes"],["dc.contributor.author","Wardetzky, Max"],["dc.date.accessioned","2017-09-07T11:53:19Z"],["dc.date.available","2017-09-07T11:53:19Z"],["dc.date.issued","2015"],["dc.description.abstract","We investigate the problem of estimating the number of modes (i.e., local maxima)—a well-known question in statistical inference—and we show how to do so without presmoothing the data. To this end, we modify the ideas of persistence barcodes by first relating persistence values in dimension one to distances (with respect to the supremum norm) to the sets of functions with a given number of modes, and subsequently working with norms different from the supremum norm. As a particular case, we investigate the Kolmogorov norm. We argue that this modification has certain statistical advantages. We offer confidence bands for the attendant Kolmogorov signatures, thereby allowing for the selection of relevant signatures with a statistically controllable error. As a result of independent interest, we show that taut strings minimize the number of critical points for a very general class of functions. We illustrate our results by several numerical examples."],["dc.identifier.doi","10.1007/s10208-015-9281-9"],["dc.identifier.gro","3145063"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/2758"],["dc.language.iso","en"],["dc.notes.intern","Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.eissn","1615-3383"],["dc.relation.issn","1615-3375"],["dc.title","Persistence Barcodes Versus Kolmogorov Signatures: Detecting Modes of One-Dimensional Signals"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1207"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society. Series B, Statistical Methodology"],["dc.bibliographiccitation.lastpage","1227"],["dc.bibliographiccitation.volume","79"],["dc.contributor.author","Pein, Florian"],["dc.contributor.author","Sieling, Hannes"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2017-09-07T11:53:20Z"],["dc.date.available","2017-09-07T11:53:20Z"],["dc.date.issued","2017"],["dc.description.abstract","We propose, a heterogeneous simultaneous multiscale change point estimator called ‘H-SMUCE’ for the detection of multiple change points of the signal in a heterogeneous Gaussian regression model. A piecewise constant function is estimated by minimizing the number of change points over the acceptance region of a multiscale test which locally adapts to changes in the variance. The multiscale test is a combination of local likelihood ratio tests which are properly calibrated by scale-dependent critical values to keep a global nominal level α, even for finite samples. We show that H-SMUCE controls the error of overestimation and underestimation of the number of change points. For this, new deviation bounds for F-type statistics are derived. Moreover, we obtain confidence sets for the whole signal. All results are non-asymptotic and uniform over a large class of heterogeneous change point models. H-SMUCE is fast to compute, achieves the optimal detection rate and estimates the number of change points at almost optimal accuracy for vanishing signals, while still being robust. We compare H-SMUCE with several state of the art methods in simulations and analyse current recordings of a transmembrane protein in the bacterial outer membrane with pronounced heterogeneity for its states. An R-package is available on line."],["dc.identifier.arxiv","1505.04898"],["dc.identifier.doi","10.1111/rssb.12202"],["dc.identifier.gro","3145061"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/2755"],["dc.language.iso","en"],["dc.notes.intern","Crossref Import"],["dc.notes.status","final"],["dc.relation.issn","1369-7412"],["dc.title","Heterogeneous change point inference"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","376"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IEEE Transactions on NanoBioscience"],["dc.bibliographiccitation.lastpage","386"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Hotz, Thomas"],["dc.contributor.author","Schütte, Ole M."],["dc.contributor.author","Sieling, Hannes"],["dc.contributor.author","Polupanow, Tatjana"],["dc.contributor.author","Diederichsen, Ulf"],["dc.contributor.author","Steinem, Claudia"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2017-09-07T11:47:00Z"],["dc.date.available","2017-09-07T11:47:00Z"],["dc.date.issued","2013"],["dc.description.abstract","Based on a combination of jump segmentation and statistical multiresolution analysis for dependent data, a new approach called J-SMURF to idealize ion channel recordings has been developed. It is model-free in the sense that no a-priori assumptions about the channel's characteristics have to be made; it thus complements existing methods which assume a model for the channel's dynamics, like hidden Markov models. The method accounts for the effect of an analog filter being applied before the data analysis, which results in colored noise, by adapting existing muliresolution statistics to this situation. J-SMURF's ability to denoise the signal without missing events even when the signal-to-noise ratio is low is demonstrated on simulations as well as on ion current traces obtained from gramicidin A channels reconstituted into solvent-free planar membranes. When analyzing a newly synthesized acylated system of a fatty acid modified gramicidin channel, we are able to give statistical evidence for unknown gating characteristics such as subgating."],["dc.identifier.doi","10.1109/TNB.2013.2284063"],["dc.identifier.gro","3142239"],["dc.identifier.isi","000330226400014"],["dc.identifier.pmid","24235310"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10112"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6076"],["dc.language.iso","en"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.eissn","1558-2639"],["dc.relation.issn","1536-1241"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Idealizing Ion Channel Recordings by a Jump Segmentation Multiresolution Filter"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dc.type.version","published_version"],["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"]]