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","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","186"],["dc.bibliographiccitation.journal","Chemometrics and Intelligent Laboratory Systems"],["dc.bibliographiccitation.lastpage","197"],["dc.bibliographiccitation.volume","146"],["dc.contributor.author","Fuchs, Karen"],["dc.contributor.author","Gertheiss, Jan"],["dc.contributor.author","Tutz, Gerhard"],["dc.date.accessioned","2018-11-07T09:53:15Z"],["dc.date.available","2018-11-07T09:53:15Z"],["dc.date.issued","2015"],["dc.description.abstract","Functional data becomes increasingly common in many fields of application. Although much research has been done on functional regression and clustering approaches for chemometric data, so far few classification methods exist. This paper introduces an ensemble method for classification that inherently provides automatic and interpretable feature selection. It is designed for single as well as multiple functional (and non-functional) covariates. The ensemble members are posterior probability estimates that are based on a k-nearest-neighbor approach. The ensemble allows for feature selection by including members that are calculated from various semi-metrics used in the k-nearest-neighbor approach, where a particular semi-metric represents a specific curve feature. Each ensemble member, and thus each curve feature, is weighted by an unknown coefficient. These coefficients are estimated using a proper scoring rule with implicit Lasso-type penalty, such that some coefficients can be estimated to be exactly zero. Thus, the ensemble automatically provides feature selection, and also, in the case of multiple functional (and non-functional) covariates, variable selection. The selection performance and the interpretability of the coefficients are investigated in simulation studies. Data of a cell chip used for water quality monitoring experiments is examined. Here, the relevance of especially the feature selection aspect of the ensemble is illustrated. (C) 2015 Elsevier B.V. All rights reserved."],["dc.description.sponsorship","Siemens AG, Corporate Technology"],["dc.identifier.doi","10.1016/j.chemolab.2015.04.019"],["dc.identifier.isi","000360595100021"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/36293"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","1873-3239"],["dc.relation.issn","0169-7439"],["dc.title","Nearest neighbor ensembles for functional data with interpretable feature selection"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","455"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Jahrbücher für Nationalökonomie und Statistik"],["dc.bibliographiccitation.lastpage","481"],["dc.bibliographiccitation.volume","236"],["dc.contributor.author","Hess, Wolfgang R."],["dc.contributor.author","Tutz, Gerhard"],["dc.contributor.author","Gertheiss, Jan"],["dc.date.accessioned","2018-11-07T10:10:42Z"],["dc.date.available","2018-11-07T10:10:42Z"],["dc.date.issued","2016"],["dc.description.abstract","This paper proposes a discrete-time hazard regression approach based on the relation between hazard rate models and excess over threshold models, which are frequently encountered in extreme value modelling. The proposed duration model employs a flexible link function and incorporates the grouped-duration analogue of the well-known Cox proportional hazards model and the proportional odds model as special cases. The theoretical setup of the model is motivated, and simulation results are reported, suggesting that the model proposed performs well. The simulation results and an empirical analysis of US import durations also show that the choice of link function in discrete hazard models has important implications for the estimation results, and that severe biases in the results can be avoided when using a flexible link function."],["dc.description.sponsorship","Jan Wallander and Tom Hedelius Foundation [W2010-0305:1]"],["dc.identifier.doi","10.1515/jbnst-2015-1022"],["dc.identifier.isi","000385014700002"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39908"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Walter De Gruyter Gmbh"],["dc.relation.issn","2366-049X"],["dc.relation.issn","0021-4027"],["dc.title","A Flexible Link Function for Discrete-Time Duration Models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]