Gertheiss, Jan

[["dc.bibliographiccitation.firstpage","157"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Statistical Modelling"],["dc.bibliographiccitation.lastpage","177"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Oelker, Margret-Ruth"],["dc.contributor.author","Gertheiss, Jan"],["dc.contributor.author","Tutz, Gerhard"],["dc.date.accessioned","2018-11-07T09:41:55Z"],["dc.date.available","2018-11-07T09:41:55Z"],["dc.date.issued","2014"],["dc.description.abstract","Varying-coefficient models with categorical effect modifiers are considered within the framework of generalized linear models. We distinguish between nominal and ordinal effect modifiers, and propose adequate Lasso-type regularization techniques that allow for (1) selection of relevant covariates, and (2) identification of coefficient functions that are actually varying with the level of a potentially effect modifying factor. For computation, a penalized iteratively reweighted least squares algorithm is presented. We investigate large sample properties of the penalized estimates; in simulation studies, we show that the proposed approaches perform very well for finite samples, too. In addition, the presented methods are compared with alternative procedures, and applied to real-world data."],["dc.description.sponsorship","DFG project 'Regularisierung fur diskrete Datenstrukturen'"],["dc.identifier.doi","10.1177/1471082X13503452"],["dc.identifier.isi","000334290800003"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33837"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Sage Publications Ltd"],["dc.relation.issn","1477-0342"],["dc.relation.issn","1471-082X"],["dc.title","Regularization and model selection with categorical predictors and effect modifiers in generalized linear models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1029"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Statistics in Medicine"],["dc.bibliographiccitation.lastpage","1041"],["dc.bibliographiccitation.volume","33"],["dc.contributor.author","Sommer, Julia C."],["dc.contributor.author","Gertheiss, Jan"],["dc.contributor.author","Schmid, Volker J."],["dc.date.accessioned","2018-11-07T09:42:33Z"],["dc.date.available","2018-11-07T09:42:33Z"],["dc.date.issued","2014"],["dc.description.abstract","Competing compartment models of different complexities have been used for the quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging data. We present a spatial elastic net approach that allows to estimate the number of compartments for each voxel such that the model complexity is not fixed apriori. A multi-compartment approach is considered, which is translated into a restricted least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial elastic net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in vivo dataset. Copyright (c) 2013 John Wiley & Sons, Ltd."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft [DFG SCHM 2747/1-1]"],["dc.identifier.doi","10.1002/sim.5997"],["dc.identifier.isi","000331395300010"],["dc.identifier.pmid","24123120"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33984"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","1097-0258"],["dc.relation.issn","0277-6715"],["dc.title","Spatially regularized estimation for the analysis of dynamic contrast-enhanced magnetic resonance imaging data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","S0309174022000341"],["dc.bibliographiccitation.firstpage","108766"],["dc.bibliographiccitation.journal","Meat Science"],["dc.bibliographiccitation.volume","188"],["dc.contributor.author","Altmann, Brianne A."],["dc.contributor.author","Gertheiss, Jan"],["dc.contributor.author","Tomasevic, Igor"],["dc.contributor.author","Engelkes, Christina"],["dc.contributor.author","Glaesener, Thibaud"],["dc.contributor.author","Meyer, Jule"],["dc.contributor.author","Schäfer, Alina"],["dc.contributor.author","Wiesen, Richard"],["dc.contributor.author","Mörlein, Daniel"],["dc.date.accessioned","2022-04-01T10:01:36Z"],["dc.date.available","2022-04-01T10:01:36Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1016/j.meatsci.2022.108766"],["dc.identifier.pii","S0309174022000341"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105706"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.relation.issn","0309-1740"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Human perception of color differences using computer vision system measurements of raw pork loin"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","232"],["dc.bibliographiccitation.journal","Meat Science"],["dc.bibliographiccitation.lastpage","236"],["dc.bibliographiccitation.volume","100"],["dc.contributor.author","Mörlein, Daniel"],["dc.contributor.author","Christensen, Rune Haubo Bojesen"],["dc.contributor.author","Gertheiss, Jan"],["dc.date.accessioned","2018-11-07T10:01:47Z"],["dc.date.available","2018-11-07T10:01:47Z"],["dc.date.issued","2015"],["dc.description.abstract","To prevent impaired consumer acceptance due to insensitive sensory quality control, it is of primary importance to periodically validate the performance of the assessors. This communication showcases how the uncertainty of sensitivity and specificity estimates is influenced by the total number of assessed samples and the prevalence of positive (here: boar tainted) samples. Furthermore, a statistically sound approach to determining the sample size that is necessary for performance validation is provided. Results show that a small sample size is associated with large uncertainty, i.e., confidence intervals and thus compromising the point estimates for assessor sensitivity. In turn, to reliably identify sensitive assessors with sufficient test power, a large sample size is needed given a certain level of confidence. Easy-to-use tables for sample size estimations are provided. (C) 2014 Elsevier Ltd. All rights reserved."],["dc.identifier.doi","10.1016/j.meatsci.2014.10.007"],["dc.identifier.isi","000347765700033"],["dc.identifier.pmid","25460131"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/38099"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1873-4138"],["dc.relation.issn","0309-1740"],["dc.relation.orgunit","Abteilung Produktqualität tierischer Erzeugnisse"],["dc.title","Validation of boar taint detection by sensory quality control: Relationship between sample size and uncertainty of performance indicators"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","447"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Biostatistics"],["dc.bibliographiccitation.lastpage","461"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Gertheiss, Jan"],["dc.contributor.author","Goldsmith, Jeff"],["dc.contributor.author","Crainiceanu, Ciprian"],["dc.contributor.author","Greven, Sonja"],["dc.date.accessioned","2018-11-07T09:23:20Z"],["dc.date.available","2018-11-07T09:23:20Z"],["dc.date.issued","2013"],["dc.description.abstract","We propose a class of estimation techniques for scalar-on-function regression where both outcomes and functional predictors may be observed at multiple visits. Our methods are motivated by a longitudinal brain diffusion tensor imaging tractography study. One of the study's primary goals is to evaluate the contemporaneous association between human function and brain imaging over time. The complexity of the study requires the development of methods that can simultaneously incorporate: (1) multiple functional (and scalar) regressors; (2) longitudinal outcome and predictor measurements per patient; (3) Gaussian or non-Gaussian outcomes; and (4) missing values within functional predictors. We propose two versions of a new method, longitudinal functional principal components regression (PCR). These methods extend the well-known functional PCR and allow for different effects of subject-specific trends in curves and of visit-specific deviations from that trend. The new methods are compared with existing approaches, and the most promising techniques are used for analyzing the tractography data."],["dc.identifier.doi","10.1093/biostatistics/kxs051"],["dc.identifier.isi","000320433000004"],["dc.identifier.pmid","23292804"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/29552"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1465-4644"],["dc.title","Longitudinal scalar-on-functions regression with application to tractography data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","258"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Journal of Agricultural Biological and Environmental Statistics"],["dc.bibliographiccitation.lastpage","277"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Gertheiss, Jan"],["dc.date.accessioned","2018-11-07T09:39:39Z"],["dc.date.available","2018-11-07T09:39:39Z"],["dc.date.issued","2014"],["dc.description.abstract","In its simplest case, ANOVA can be seen as a generalization of the t-test for comparing the means of a continuous variable in more than two groups defined by the levels of a discrete covariate, a so-called factor. Testing is then typically done by using the standard F-test. Here, we consider the special but frequent case of factor levels that are ordered. We propose an alternative test using mixed models methodology. The new test often outperforms the standard F-test when factor levels are ordered. We illustrate the proposed testing procedure in simulation studies and three typical applications: nonparametric dose response analysis in agriculture, associations between rating scales and a continuous outcome, and testing differentially expressed genes with ordinal phenotypes."],["dc.identifier.doi","10.1007/s13253-014-0170-5"],["dc.identifier.isi","000336398300006"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33330"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1537-2693"],["dc.relation.issn","1085-7117"],["dc.title","ANOVA for Factors With Ordered Levels"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","255"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Meat Science"],["dc.bibliographiccitation.lastpage","262"],["dc.bibliographiccitation.volume","98"],["dc.contributor.author","Trautmann, Johanna"],["dc.contributor.author","Gertheiss, Jan"],["dc.contributor.author","Wicke, Michael"],["dc.contributor.author","Mörlein, Daniel"],["dc.date.accessioned","2018-11-07T09:34:59Z"],["dc.date.available","2018-11-07T09:34:59Z"],["dc.date.issued","2014"],["dc.description.abstract","Due to animal welfare concerns the production of entire male pigs is one viable alternative to surgical castration. Elevated levels of boar taint may, however, impair consumer acceptance. Due to the lack of technical methods, control of boar taint is currently done using sensory quality control. While the need for control measures with respect to boar taint has been clearly stated in EU legislation, no specific requirements for selecting assessors have yet been documented. This study proposes tests for the psychophysical evaluation of olfactory acuity to key volatiles contributing to boar taint. Odor detection thresholds for androstenone and skatole are assessed as well as the subject's ability to identify odorants at various levels through easy-to-use paper smell strips. Subsequently, fat samples are rated by the assessors, and the accuracy of boar taint evaluation is studied. Considerable variation of olfactory performance is observed demonstrating the need for objective criteria to select assessors. (C) 2014 Elsevier Ltd. All rights reserved."],["dc.identifier.doi","10.1016/j.meatsci.2014.05.037"],["dc.identifier.isi","000340333500026"],["dc.identifier.pmid","24976560"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32295"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1873-4138"],["dc.relation.issn","0309-1740"],["dc.relation.orgunit","Abteilung Produktqualität tierischer Erzeugnisse"],["dc.title","How olfactory acuity affects the sensory assessment of boar fat: A proposal for quantification"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","436"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Methods of Information in Medicine"],["dc.bibliographiccitation.lastpage","445"],["dc.bibliographiccitation.volume","53"],["dc.contributor.author","Bühlmann, Peter"],["dc.contributor.author","Gertheiss, Jan"],["dc.contributor.author","Hieke, S."],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Ma, S."],["dc.contributor.author","Schumacher, M."],["dc.contributor.author","Tutz, Gerhard"],["dc.contributor.author","Wang, C.-Y."],["dc.contributor.author","Wang, Z."],["dc.contributor.author","Ziegler, Andreas"],["dc.date.accessioned","2018-11-07T09:45:33Z"],["dc.date.available","2018-11-07T09:45:33Z"],["dc.date.issued","2014"],["dc.description.abstract","This article is part of a For-Discussion-Section of Methods of Information in Medicine about the papers \"The Evolution of Boosting Algorithms From Machine Learning to Statistical Modelling\" [1] and \"Extending Statistical Boosting An Overview of Recent Methodological Developments\" [2], written by Andreas Mayr and co-authors. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Mayr et al. papers. In subsequent issues the discussion can continue through letters to the editor."],["dc.identifier.doi","10.3414/13100122"],["dc.identifier.isi","000346393200004"],["dc.identifier.pmid","25396219"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34647"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","0026-1270"],["dc.title","Discussion of \"The Evolution of Boosting Algorithms\" and \"Extending Statistical Boosting\""],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","357"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Psychometrika"],["dc.bibliographiccitation.lastpage","376"],["dc.bibliographiccitation.volume","79"],["dc.contributor.author","Tutz, Gerhard"],["dc.contributor.author","Gertheiss, Jan"],["dc.date.accessioned","2018-11-07T09:37:54Z"],["dc.date.available","2018-11-07T09:37:54Z"],["dc.date.issued","2014"],["dc.description.abstract","Rating scales as predictors in regression models are typically treated as metrically scaled variables or, alternatively, are coded in dummy variables. The first approach implies a scale level that is not justified, the latter approach results in a large number of parameters to be estimated. Therefore, when rating scales are dummy-coded, applications are often restricted to the use of a few predictors. The penalization approach advocated here takes the scale level serious by using only the ordering of categories but is shown to work in the high dimensional case. We consider the proper modeling of rating scales as predictors and selection procedures by using penalization methods that are tailored to ordinal predictors. In addition to the selection of predictors, the clustering of categories is investigated. Existing methodology is extended to the wider class of generalized linear models. Moreover, higher order differences that allow shrinkage towards a polynomial as well as monotonicity constraints and alternative penalties are introduced. The proposed penalization approaches are illustrated by use of the Motivational States Questionnaire."],["dc.identifier.doi","10.1007/s11336-013-9343-3"],["dc.identifier.isi","000342072100002"],["dc.identifier.pmid","25205003"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32950"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1860-0980"],["dc.relation.issn","0033-3123"],["dc.title","Rating Scales as Predictors-The Old Question of Scale Level and Some Answers"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","34"],["dc.bibliographiccitation.journal","Meat Science"],["dc.bibliographiccitation.lastpage","42"],["dc.bibliographiccitation.volume","118"],["dc.contributor.author","Meier-Dinkel, Lisa"],["dc.contributor.author","Gertheiss, Jan"],["dc.contributor.author","Schnäckel, Wolfram"],["dc.contributor.author","Mörllein, Daniel"],["dc.date.accessioned","2018-11-07T10:11:10Z"],["dc.date.available","2018-11-07T10:11:10Z"],["dc.date.issued","2016"],["dc.description.abstract","Characteristic off-flavours may occur in uncastrated male pigs depending on the accumulation of androstenone and skatole. Feasible processing of strongly tainted carcasses is challenging but gains in importance due to the European ban on piglet castration in 2018. This paper investigates consumers' acceptability of two sausage types: (a) emulsion-type (BOILED) and (b) smoked raw-fermented (FERM). Liking (9 point scales) and flavour perception (check-all-that-apply with both, typical and negatively connoted sensory terms) were evaluated by 120 consumers (within-subject design). Proportion of tainted boar meat (0, 50,100%) affected overall liking of BOILED, F (2, 238) = 23.22, P < .001, but not of FERM sausages, F (2, 238) = 0.89, P = .414. Consumers described the flavour of BOILED-100 as strong and sweaty. In conclusion, FERM products seem promising for processing of tainted carcasses whereas formulations must be optimized for BOILED in order to eliminate perceptible off flavours. Boar taint rejection thresholds may be higher for processed than those suggested for unprocessed meat cuts. (C) 2016 Elsevier Ltd. All rights reserved."],["dc.description.sponsorship","Federal Ministry of Food and Agriculture (BMEL) [FKZ 11OE143]"],["dc.identifier.doi","10.1016/j.meatsci.2016.03.018"],["dc.identifier.isi","000380581100006"],["dc.identifier.pmid","27038338"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39994"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation","BÖLN Ebermast"],["dc.relation.issn","1873-4138"],["dc.relation.issn","0309-1740"],["dc.relation.orgunit","Abteilung Produktqualität tierischer Erzeugnisse"],["dc.title","Consumers' perception and acceptance of boiled and fermented sausages from strongly boar tainted meat"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]