Hohberg, Maike

[["dc.contributor.author","Hohberg, M."],["dc.contributor.author","Donat, F."],["dc.contributor.author","Marra, G."],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-06T12:06:26Z"],["dc.date.available","2020-04-06T12:06:26Z"],["dc.date.issued","2019"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63846"],["dc.title","Beyond unidimensional poverty analysis using distributional copula models for mixed ordered-continuous outcomes"],["dc.type","preprint"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Hohberg, Maike"],["dc.contributor.author","Pütz, Peter"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-03T10:44:10Z"],["dc.date.available","2020-04-03T10:44:10Z"],["dc.date.issued","2018-06"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63582"],["dc.title","Generalized additive models for location, scale and shape for program evaluation"],["dc.title.subtitle","A guide to practice"],["dc.type","preprint"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0226514"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","PLoS One"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Hohberg, Maike"],["dc.contributor.author","Pütz, Peter"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-06T09:15:47Z"],["dc.date.available","2020-04-06T09:15:47Z"],["dc.date.issued","2020"],["dc.description.abstract","This paper introduces distributional regression also known as generalized additive models for location, scale and shape (GAMLSS) as a modeling framework for analyzing treatment effects beyond the mean. In contrast to mean regression models, GAMLSS relate each distributional parameter to covariates. Therefore, they can be used to model the treatment effect not only on the mean but on the whole conditional distribution. Since they encompass a wide range of different distributions, GAMLSS provide a flexible framework for modeling non-normal outcomes in which additionally nonlinear and spatial effects can easily be incorporated. We elaborate on the combination of GAMLSS with program evaluation methods including randomized controlled trials, panel data techniques, difference in differences, instrumental variables, and regression discontinuity design. We provide practical guidance on the usage of GAMLSS by reanalyzing data from the Mexican Progresa program. Contrary to expectations, no significant effects of a cash transfer on the conditional consumption inequality level between treatment and control group are found."],["dc.identifier.doi","10.1371/journal.pone.0226514"],["dc.identifier.pmid","32058999"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17341"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63830"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","1932-6203"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Treatment effects beyond the mean using distributional regression: Methods and guidance"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1553"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society: Series A (Statistics in Society)"],["dc.bibliographiccitation.lastpage","1574"],["dc.bibliographiccitation.volume","183"],["dc.contributor.author","Briseño Sanchez, Guillermo"],["dc.contributor.author","Hohberg, Maike"],["dc.contributor.author","Groll, Andreas"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2021-04-14T08:24:04Z"],["dc.date.available","2021-04-14T08:24:04Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1111/rssa.12598"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81150"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1467-985X"],["dc.relation.issn","0964-1998"],["dc.title","Flexible instrumental variable distributional regression"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","231"],["dc.bibliographiccitation.lastpage","255"],["dc.contributor.author","Hohberg, Maike"],["dc.contributor.author","Silbersdorff, Alexander"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-06T07:35:08Z"],["dc.date.available","2020-04-06T07:35:08Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1007/978-3-658-16145-3_10"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63774"],["dc.relation.isbn","978-3-658-16144-6"],["dc.relation.isbn","978-3-658-16145-3"],["dc.relation.ispartof","Perspektiven einer pluralen Ökonomik"],["dc.title","Mehr als Durchschnittsstatistik"],["dc.title.subtitle","Eine kritische Einführung in Regressionsmethoden jenseits des Mittelwertes"],["dc.type","book_chapter"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e000408"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMJ Open Sport & Exercise Medicine"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Krüger, Lara"],["dc.contributor.author","Hohberg, Maike"],["dc.contributor.author","Lehmann, Wolfgang"],["dc.contributor.author","Dresing, Klaus"],["dc.date.accessioned","2019-07-09T11:49:39Z"],["dc.date.available","2019-07-09T11:49:39Z"],["dc.date.issued","2018"],["dc.description.abstract","Background/aim: Horse riding is a popular sport, which bears the risk of serious injuries. This study aims to assess whether individual factors influence the risk to sustain major injuries. Methods: Retrospective data were collected from all equine-related accidents at a German Level I Trauma Centre between 2004 and 2014. Logistic regression was used to identify the risk factors for major injures. Results: 770 patients were included (87.9% females). Falling off the horse (67.7%) and being kicked by the horse (16.5%) were the two main injury mechanisms. Men and individuals of higher age showed higher odds for all tested parameters of serious injury. Patients falling off a horse had higher odds for being treated as inpatients, whereas patients who were kicked had higher odds for a surgical therapy (OR 1.7) and intensive care unit/intermediate care unit (ICU/IMC) treatment (OR 1.2). The head was the body region most often injured (32.6%) and operated (32.9%). Patients with head injuries had the highest odds for being hospitalised (OR 6.13). Head or trunk injuries lead to the highest odds for an ICU/IMC treatment (head: OR 4.37; trunk: OR 2.47). Upper and lower limb injuries showed the highest odds for a surgical therapy (upper limb: OR 2.61; lower limb: OR 1.7). Conclusion: Risk prevention programmes should include older individuals and males as target groups. Thus a rethinking of the overall risk assessment is necessary. Not only horseback riding itself, but also handling a horse bears a relevant risk for major injuries. Serious head injures remain frequent, serious and an important issue to be handled in equestrians sports."],["dc.identifier.doi","10.1136/bmjsem-2018-000408"],["dc.identifier.pmid","30364519"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15731"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59597"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY-NC 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc/4.0"],["dc.subject.ddc","610"],["dc.title","Assessing the risk for major injuries in equestrian sports"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1365"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society: Series C (Applied Statistics)"],["dc.bibliographiccitation.lastpage","1390"],["dc.bibliographiccitation.volume","70"],["dc.contributor.affiliation","Donat, Francesco; 2\r\nSingle Resolution Board\r\nBrussels Belgium"],["dc.contributor.affiliation","Marra, Giampiero; 3\r\nDepartment of Statistical Science\r\nUniversity College London\r\nLondon UK"],["dc.contributor.affiliation","Kneib, Thomas; 1\r\nChair of Statistics\r\nUniversity of Goettingen\r\nGottingen Germany"],["dc.contributor.author","Hohberg, Maike"],["dc.contributor.author","Donat, Francesco"],["dc.contributor.author","Marra, Giampiero"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2021-12-01T09:21:15Z"],["dc.date.available","2021-12-01T09:21:15Z"],["dc.date.issued","2021"],["dc.date.updated","2022-03-21T07:44:48Z"],["dc.description.abstract","Abstract Poverty is a multidimensional concept often comprising a monetary outcome and other welfare dimensions such as education, subjective well‐being or health that are measured on an ordinal scale. In applied research, multidimensional poverty is ubiquitously assessed by studying each poverty dimension independently in univariate regression models or by combining several poverty dimensions into a scalar index. This approach inhibits a thorough analysis of the potentially varying interdependence between the poverty dimensions. We propose a multivariate copula generalized additive model for location, scale and shape (copula GAMLSS or distributional copula model) to tackle this challenge. By relating the copula parameter to covariates, we specifically examine if certain factors determine the dependence between poverty dimensions. Furthermore, specifying the full conditional bivariate distribution allows us to derive several features such as poverty risks and dependence measures coherently from one model for different individuals. We demonstrate the approach by studying two important poverty dimensions: income and education. Since the level of education is measured on an ordinal scale while income is continuous, we extend the bivariate copula GAMLSS to the case of mixed ordered‐continuous outcomes. The new model is integrated into the GJRM package in R and applied to data from Indonesia. Particular emphasis is given to the spatial variation of the income–education dependence and groups of individuals at risk of being simultaneously poor in both education and income dimensions."],["dc.identifier.doi","10.1111/rssc.12517"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94390"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.relation.eissn","1467-9876"],["dc.relation.issn","0035-9254"],["dc.rights","This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made."],["dc.rights.uri","http://creativecommons.org/licenses/by-nc-nd/4.0/"],["dc.title","Beyond unidimensional poverty analysis using distributional copula models for mixed ordered‐continuous outcomes"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","IZA Journal of Labor & Development"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Hohberg, Maike"],["dc.contributor.author","Lay, Jann"],["dc.date.accessioned","2019-07-09T11:41:29Z"],["dc.date.available","2019-07-09T11:41:29Z"],["dc.date.issued","2015"],["dc.description.abstract","This paper studies the effects of minimum wages on informal and formal sector wages and employment in Indonesia between 1997 and 2007. Applying fixed-effects methods, the estimates suggest that minimum wages have a significant positive effect on formal sector wages, while there are no spillover effects on informal workers. Regarding employment, we find no statistically significant negative effects of minimum wages on the probability of being formally employed. These findings suggest that employers use adjustment channels other than employment or that effects such as a demand stimulus on a local level outweigh the possible negative employment effects."],["dc.identifier.doi","10.1186/s40175-015-0036-4"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12096"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58441"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","2193-9020"],["dc.relation.orgunit","Wirtschaftswissenschaftliche Fakultät"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","The impact of minimum wages on informal and formal labor market outcomes: evidence from Indonesia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","The Journal of Economic Inequality"],["dc.bibliographiccitation.lastpage","16"],["dc.contributor.author","Hohberg, Maike"],["dc.contributor.author","Landau, Katja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Klasen, Stephan"],["dc.contributor.author","Zucchini, Walter"],["dc.date.accessioned","2018-03-13T13:29:21Z"],["dc.date.available","2018-03-13T13:29:21Z"],["dc.date.issued","2018"],["dc.description.abstract","This paper analyzes several modifications to improve a simple measure of vulnerability as expected poverty. Firstly, in order to model income, we apply distributional regression relating potentially each parameter of the conditional income distribution to the covariates. Secondly, we determine the vulnerability cutoff endogenously instead of defining a household as vulnerable if its probability of being poor in the next period is larger than 0.5. For this purpose, we employ the receiver operating characteristic curve that is able to consider prerequisites according to a particular targeting mechanism. Using long-term panel data from Germany, we build both mean and distributional regression models with the established 0.5 probability cutoff and our vulnerability cutoff. We find that our new cutoff considerably increases predictive performance. Placing the income regression model into the distributional regression framework does not improve predictions further but has the advantage of a coherent model where parameters are estimated simultaneously replacing the original three step estimation approach."],["dc.identifier.doi","10.1007/s10888-017-9374-6"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15560"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/12978"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.relation.orgunit","Wirtschaftswissenschaftliche Fakultät"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Vulnerability to poverty revisited: Flexible modeling and better predictive performance"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","187"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Medical Research Methodology"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Martins, Rui"],["dc.contributor.author","Sousa, Bruno d."],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Hohberg, Maike"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Duarte, Elisa"],["dc.contributor.author","Rodrigues, Vítor"],["dc.date.accessioned","2022-08-04T12:02:10Z"],["dc.date.available","2022-08-04T12:02:10Z"],["dc.date.issued","2022-07-11"],["dc.date.updated","2022-07-25T11:18:52Z"],["dc.description.abstract","Background Due to contradictory results in current research, whether age at menopause is increasing or decreasing in Western countries remains an open question, yet worth studying as later ages at menopause are likely to be related to an increased risk of breast cancer. Using data from breast cancer screening programs to study the temporal trend of age at menopause is difficult since especially younger women in the same generational cohort have often not yet reached menopause. Deleting these younger women in a breast cancer risk analyses may bias the results. The aim of this study is therefore to recover missing menopause ages as a covariate by comparing methods for handling missing data. Additionally, the study makes a contribution to understanding the evolution of age at menopause for several generations born in Portugal between 1920 and 1970. Methods Data from a breast cancer screening program in Portugal including 278,282 women aged 45–69 and collected between 1990 and 2010 are used to compare two approaches of imputing age at menopause: (i) a multiple imputation methodology based on a truncated distribution but ignoring the mechanism of missingness; (ii) a copula-based multiple imputation method that simultaneously handles the age at menopause and the missing mechanism. The linear predictors considered in both cases have a semiparametric additive structure accommodating linear and non-linear effects defined via splines or Markov random fields smoothers in the case of spatial variables. Results Both imputation methods unveiled an increasing trend of age at menopause when viewed as a function of the birth year for the youngest generation. This trend is hidden if we model only women with an observed age at menopause. Conclusion When studying age at menopause, missing ages must be recovered with an adequate procedure for incomplete data. Imputing these missing ages avoids excluding the younger generation cohort of the screening program in breast cancer risk analyses and hence reduces the bias stemming from this exclusion. In addition, imputing the not yet observed ages of menopause for mostly younger women is also crucial when studying the time trend of age at menopause otherwise the analysis will be biased."],["dc.identifier.citation","BMC Medical Research Methodology. 2022 Jul 11;22(1):187"],["dc.identifier.doi","10.1186/s12874-022-01658-x"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112645"],["dc.language.iso","en"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Copula function"],["dc.subject","Distributional regression"],["dc.subject","GJRM"],["dc.subject","Incomplete data"],["dc.subject","Menopause"],["dc.subject","Smoothing"],["dc.title","Is age at menopause decreasing? – The consequences of not completing the generational cohort"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]