Silbersdorff, Alexander

[["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Michelot, Théo"],["dc.contributor.author","Sohn, Alexander"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-03T13:30:36Z"],["dc.date.available","2020-04-03T13:30:36Z"],["dc.date.issued","2013-08-27"],["dc.description.abstract","Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption of conditionally normally distributed or Student-t-distributed returns, given the volatility, has however been questioned. In this manuscript, we introduce a novel maximum penalized likelihood approach for estimating the conditional distribution in an SV model in a nonparametric way, thus avoiding any potentially critical assumptions on the shape. The considered framework exploits the strengths both of the powerful hidden Markov model machinery and of penalized B-splines, and constitutes a powerful and flexible alternative to recently developed Bayesian approaches to semiparametric SV modelling. We demonstrate the feasibility of the approach in a simulation study before outlining its potential in applications to three series of returns on stocks and one series of stock index returns."],["dc.identifier.arxiv","1308.5836v3"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63632"],["dc.title","Semiparametric stochastic volatility modelling using penalized splines"],["dc.type","preprint"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","European Child & Adolescent Psychiatry"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Becker, Andreas"],["dc.contributor.author","Rothenberger, Aribert"],["dc.contributor.author","Sohn, Alexander"],["dc.contributor.author","Ravens-Sieberer, Ulrike"],["dc.contributor.author","Klasen, Fionna"],["dc.date.accessioned","2018-11-07T09:56:30Z"],["dc.date.available","2018-11-07T09:56:30Z"],["dc.date.issued","2015"],["dc.format.extent","727"],["dc.identifier.doi","10.1007/s00787-015-0706-4"],["dc.identifier.isi","000355660300013"],["dc.identifier.pmid","25862434"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/36963"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1435-165X"],["dc.relation.issn","1018-8827"],["dc.title","Six years ahead: a longitudinal analysis regarding course and predictive value of the Strengths and Difficulties Questionnaire (SDQ) in children and adolescents (vol 24, pg 715, 2015)"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1074"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Health Economics"],["dc.bibliographiccitation.lastpage","1088"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Silbersdorff, A."],["dc.contributor.author","Lynch, Julia"],["dc.contributor.author","Klasen, Stephan"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-06T06:44:34Z"],["dc.date.available","2020-04-06T06:44:34Z"],["dc.date.issued","2018"],["dc.description.abstract","We reconsider the relationship between income and health taking a distributional perspective rather than one centered on conditional expectation. Using structured additive distributional regression, we find that the association between income and health is larger than generally estimated because aspects of the conditional health distribution that go beyond the expectation imply worse outcomes for those with lower incomes. Looking at German data from the Socio-Economic Panel, we find that the risk of bad health is roughly halved when doubling the net equivalent income from 15,000 to 30,000€. This is more than tenfold of the magnitude of change found when considering expected health measures. A distributional perspective thus highlights another dimension of the income-health relation-that the poor are in particular faced with greater health risk at the lower end of the health distribution. We therefore argue that when studying health outcomes, a distributional approach that considers stochastic variation among observationally equivalent individuals is warranted."],["dc.identifier.doi","10.1002/hec.3656"],["dc.identifier.pmid","29676015"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63740"],["dc.language.iso","en"],["dc.relation.eissn","1099-1050"],["dc.relation.issn","1057-9230"],["dc.title","Reconsidering the income-health relationship using distributional regression"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Sohn, A."],["dc.contributor.author","DeRuiter, Stacy"],["dc.date.accessioned","2020-04-03T13:28:34Z"],["dc.date.available","2020-04-03T13:28:34Z"],["dc.date.issued","2013-09-02"],["dc.description.abstract","Hidden Markov models (HMMs) are flexible time series models in which the distributions of the observations depend on unobserved serially correlated states. The state-dependent distributions in HMMs are usually taken from some class of parametrically specified distributions. The choice of this class can be difficult, and an unfortunate choice can have serious consequences for example on state estimates, on forecasts and generally on the resulting model complexity and interpretation, in particular with respect to the number of states. We develop a novel approach for estimating the state-dependent distributions of an HMM in a nonparametric way, which is based on the idea of representing the corresponding densities as linear combinations of a large number of standardized B-spline basis functions, imposing a penalty term on non-smoothness in order to maintain a good balance between goodness-of-fit and smoothness. We illustrate the nonparametric modeling approach in a real data application concerned with vertical speeds of a diving beaked whale, demonstrating that compared to parametric counterparts it can lead to models that are more parsimonious in terms of the number of states yet fit the data equally well."],["dc.identifier.arxiv","1309.0423v2"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63629"],["dc.title","Nonparametric inference in hidden Markov models using P-splines"],["dc.type","preprint"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","715"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","European Child & Adolescent Psychiatry"],["dc.bibliographiccitation.lastpage","725"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Becker, Andreas"],["dc.contributor.author","Rothenberger, Aribert"],["dc.contributor.author","Sohn, Alexander"],["dc.date.accessioned","2018-11-07T09:56:29Z"],["dc.date.available","2018-11-07T09:56:29Z"],["dc.date.issued","2015"],["dc.description.abstract","Scientifically sound and valid information concerning course and prediction of mental health problems in children and adolescents in the general population is scarce, although needed for public mental health issues and daily clinical practice. The psychopathological profiles of children and adolescents were analysed using the parent version of the Strengths and Difficulties Questionnaire (SDQ-P) in a longitudinal setting, also investigating the predictive value of the SDQ-scores. SDQ's total psychopathological difficulties, emotional symptoms and hyperactivity-inattention scores of n = 630 children and adolescents (age 6-18;11 years) were examined along four assessment measurement points (T-0-T-3) over 6 years, using data from the BELLA study. According to the English normative data, the participants were categorized as \"normal\", \"borderline\" or \"abnormal\" based on their SDQ-scores. Groups remaining within categories were descriptively determined by means of frequency analysis, a subsequent graphical evaluation displayed the transitions from T-0 to T-3 concerning the different categorical classifications. Finally, ordered probit regression was used to examine whether age, gender, socio-economic status (SES) and baseline impact-score (IS) correspond to the SDQ-predicted classification. As expected, low SES and high SDQ-IS were associated with significantly increased scores on all examined SDQ-scales. Regarding the long-term aspect of SDQ-scores it could be shown that most children and adolescents remained \"normal\" over a measurement period of 6 years, while only a small number of children and adolescents steadily remained \"abnormal\" or newly developed mental health problems, respectively. For example, on the \"hyperactivity-inattention\"-scale, only 1 % of the children and adolescents changed from \"normal\" to \"abnormal\" (T-0-T-3), whereas on the \"emotional symptoms\"-scale, 7 % changed from \"normal\" to \"abnormal\" (T-0-T-3). In general, the SDQ-category \"borderline\" and specifically the subscale \"emotional symptoms\" change in both directions. Abnormal SDQ-scores at baseline, SES, gender and IS were related to the prediction of the SDQ-sores at T3. An SDQ-screening of children and adolescents may help for early detection, prediction and treatment planning. Also, these results may contribute to a better understanding of the course of mental health problems in childhood and concurrently may allow a better psychoeducation and prevention."],["dc.description.sponsorship","Shire; German Research Society; Schwaabe"],["dc.identifier.doi","10.1007/s00787-014-0640-x"],["dc.identifier.isi","000355660300012"],["dc.identifier.pmid","25450722"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/36962"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1435-165X"],["dc.relation.issn","1018-8827"],["dc.title","Six years ahead: a longitudinal analysis regarding course and predictive value of the Strengths and Difficulties Questionnaire (SDQ) in children and adolescents"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","84"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Scandinavian Economic History Review"],["dc.bibliographiccitation.lastpage","102"],["dc.bibliographiccitation.volume","64"],["dc.contributor.author","Sohn, Alexander"],["dc.date.accessioned","2020-12-10T18:14:52Z"],["dc.date.available","2020-12-10T18:14:52Z"],["dc.date.issued","2016"],["dc.identifier.doi","10.1080/03585522.2016.1175374"],["dc.identifier.eissn","1750-2837"],["dc.identifier.issn","0358-5522"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/74647"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Poor university professors? The relative earnings decline of German professors during the twentieth century"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Sohn, Alexander"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2020-04-06T09:21:01Z"],["dc.date.available","2020-04-06T09:21:01Z"],["dc.date.issued","2014"],["dc.identifier.doi","10.2139/ssrn.2404335"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63833"],["dc.title","A New Semiparametric Approach to Analysing Conditional Income Distributions"],["dc.type","report"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","4009"],["dc.bibliographiccitation.issue","20"],["dc.bibliographiccitation.journal","International Journal of Environmental Research and Public Health"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Silbersdorff, Alexander"],["dc.contributor.author","Schneider, Kai Sebastian"],["dc.date.accessioned","2020-12-10T18:47:07Z"],["dc.date.available","2020-12-10T18:47:07Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.3390/ijerph16204009"],["dc.identifier.eissn","1660-4601"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16915"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/78651"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","1660-4601"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Distributional Regression Techniques in Socioeconomic Research on the Inequality of Health with an Application on the Relationship between Mental Health and Income"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","S0167947321001857"],["dc.bibliographiccitation.firstpage","107351"],["dc.bibliographiccitation.journal","Computational Statistics & Data Analysis"],["dc.bibliographiccitation.volume","167"],["dc.contributor.author","Kruse, René-Marcel"],["dc.contributor.author","Silbersdorff, Alexander"],["dc.contributor.author","Säfken, Benjamin"],["dc.date.accessioned","2021-12-01T09:23:33Z"],["dc.date.available","2021-12-01T09:23:33Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1016/j.csda.2021.107351"],["dc.identifier.pii","S0167947321001857"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94686"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.relation.issn","0167-9473"],["dc.title","Model averaging for linear mixed models via augmented Lagrangian"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","520"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Biometrics"],["dc.bibliographiccitation.lastpage","528"],["dc.bibliographiccitation.volume","71"],["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Sohn, Alexander"],["dc.contributor.author","Ruiter, Stacy L. de"],["dc.date.accessioned","2017-09-07T11:47:19Z"],["dc.date.available","2017-09-07T11:47:19Z"],["dc.date.issued","2015"],["dc.identifier.doi","10.1111/biom.12282"],["dc.identifier.gro","3149327"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5991"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Wiley-Blackwell"],["dc.relation.issn","0006-341X"],["dc.title","Nonparametric inference in hidden Markov models using P-splines"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]