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.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.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","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"]]

[["dc.contributor.author","Hambuckers, Julien"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Langrock, R"],["dc.contributor.author","Silbersdorff, A."],["dc.date.accessioned","2020-04-03T10:57:11Z"],["dc.date.available","2020-04-03T10:57:11Z"],["dc.date.issued","2017-12"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63585"],["dc.title","A Markov-Switching Generalized Additive Model for Compound Poisson Processes, with Applications to Operational Losses Models"],["dc.type","preprint"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","13"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Schmollers Jahrbuch"],["dc.bibliographiccitation.lastpage","22"],["dc.bibliographiccitation.volume","135"],["dc.contributor.author","Sohn, Alexander"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2017-09-07T11:47:47Z"],["dc.date.available","2017-09-07T11:47:47Z"],["dc.date.issued","2016"],["dc.identifier.doi","10.3790/schm.135.1.13"],["dc.identifier.gro","3149361"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6029"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1439-121X"],["dc.title","A Semiparametric Analysis of Conditional Income Distributions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1135"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","The Annals of Applied Statistics"],["dc.bibliographiccitation.lastpage","1136"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Lang, Stefan"],["dc.contributor.author","Sohn, Alexander"],["dc.date.accessioned","2020-12-10T18:41:47Z"],["dc.date.available","2020-12-10T18:41:47Z"],["dc.date.issued","2016"],["dc.identifier.doi","10.1214/16-AOAS922"],["dc.identifier.fs","622641"],["dc.identifier.issn","1932-6157"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77673"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.status","final"],["dc.relation.iserratumof","/handle/2/6000"],["dc.relation.issn","1932-6157"],["dc.title","Correction: Bayesian structured additive distributional regression with an application to regional income inequality in Germany"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","unknown"],["dc.type.subtype","erratum_ja"],["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.firstpage","517"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Computational Statistics"],["dc.bibliographiccitation.lastpage","537"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Michelot, Théo"],["dc.contributor.author","Sohn, Alexander"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2017-09-07T11:47:45Z"],["dc.date.available","2017-09-07T11:47:45Z"],["dc.date.issued","2014"],["dc.identifier.doi","10.1007/s00180-014-0547-5"],["dc.identifier.gro","3149348"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6015"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Springer Nature"],["dc.relation.issn","0943-4062"],["dc.title","Semiparametric stochastic volatility modelling using penalized splines"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]