Krivobokova, Tatyana

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Computational and Graphical Statistics"],["dc.bibliographiccitation.lastpage","20"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Krivobokova, Tatyana"],["dc.contributor.author","Crainiceanu, Ciprian M."],["dc.contributor.author","Kauermann, Göran"],["dc.date.accessioned","2017-09-07T11:50:04Z"],["dc.date.available","2017-09-07T11:50:04Z"],["dc.date.issued","2008"],["dc.description.abstract","This article proposes a numerically simple method for locally adaptive smoothing. The heterogeneous regression function is modeled as a penalized spline with a varying smoothing parameter modeled as another penalized spline. This is formulated as a hierarchical mixed model, with spline coefficients following zero mean normal distribution with a smooth variance structure. The major contribution of this article is to use the Laplace approximation of the marginal likelihood for estimation. This method is numerically simple and fast. The idea is extended to spatial and non-normal response smoothing."],["dc.identifier.doi","10.1198/106186008x287328"],["dc.identifier.gro","3145860"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3591"],["dc.notes.intern","mathe"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Informa UK Limited"],["dc.relation.issn","1061-8600"],["dc.subject","Function of locally varying complexity Hierarchical mixed model Laplace approximation"],["dc.title","Fast Adaptive Penalized Splines"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Studies in Nonlinear Dynamics & Econometrics"],["dc.bibliographiccitation.lastpage","28"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Kauermann, Göran"],["dc.contributor.author","Krivobokova, Tatyana"],["dc.contributor.author","Semmler, Willi"],["dc.date.accessioned","2017-09-07T11:50:08Z"],["dc.date.available","2017-09-07T11:50:08Z"],["dc.date.issued","2011"],["dc.description.abstract","The decomposition and filtering of time series is an important issue in economics and econometrics and related fields. Even though there are numerous competing methods on the market, in applications one often meets one of the few favorites, like the Hodrick-Prescott filter or the bandpass filter.In this paper, we suggest to employ penalized splines fitting for detrending. The approach allows to take correlation of the residuals into account and provides a data driven setting of the smoothing parameter, none of which the classical filters allow. We show the simplicity of the penalized spline filter using the open source software R and demonstrate differences and features with numerous data examples."],["dc.identifier.doi","10.2202/1558-3708.1789"],["dc.identifier.gro","3145856"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8655"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3586"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1558-3708"],["dc.relation.orgunit","Fakultät für Mathematik und Informatik"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Filtering Time Series with Penalized Splines"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","443"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Statistical Papers"],["dc.bibliographiccitation.lastpage","459"],["dc.bibliographiccitation.volume","47"],["dc.contributor.author","Krivobokova, Tatyana"],["dc.contributor.author","Kauermann, Göran"],["dc.contributor.author","Archontakis, Theofanis"],["dc.date.accessioned","2017-09-07T11:50:04Z"],["dc.date.available","2017-09-07T11:50:04Z"],["dc.date.issued","2006"],["dc.description.abstract","We analyse the term structure of interest rates extracted from US Treasury STRIPS data. There is a potential interest from a scientific and economic point of view to look at short and long term bonds simultaneously. In terms of modelling this means to look at smooth functions over time describing the observed term structure. This is the approach pursued in this paper, where penalized spline fitting is employed as smoothing technique.. Smoothing is thereby carried out with the respect to both, calendar time and time left to maturity. While the first reveals long term trends, smoothing with respect to the time left to maturity can conceptionally be interpreted as interpolation. Since term structure models have implications for both, the time series and cross-section dimension of yields, estimation techniques involving both dimensions simultaneously are preferred over one-dimensional techniques. Numerical parsimony is applied to fit the large data set and smoothing parameter selection is pursued by building up parallels to linear mixed models."],["dc.identifier.doi","10.1007/s00362-006-0297-8"],["dc.identifier.gro","3145862"],["dc.identifier.pmid","1599502"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3593"],["dc.notes.intern","mathe"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Springer Nature"],["dc.relation.issn","0932-5026"],["dc.title","Estimating the term structure of interest rates using penalized splines"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1328"],["dc.bibliographiccitation.issue","480"],["dc.bibliographiccitation.journal","Journal of the American Statistical Association"],["dc.bibliographiccitation.lastpage","1337"],["dc.bibliographiccitation.volume","102"],["dc.contributor.author","Krivobokova, Tatyana"],["dc.contributor.author","Kauermann, Göran"],["dc.date.accessioned","2017-09-07T11:50:04Z"],["dc.date.available","2017-09-07T11:50:04Z"],["dc.date.issued","2007"],["dc.description.abstract","We investigate the behavior of data-driven smoothing parameters for penalized spline regression in the presence of correlated data. It has been shown for other smoothing methods that mean squared error minimizers, such as (generalized) cross-validation or the Akaike information criterion, are extremely sensitive to misspecifications of the correlation structure resulting in over- or (under-)fitting the data. In contrast to this, we show that a maximum likelihood-based choice of the smoothing parameter is more robust and that for a moderately misspecified correlation structure over- or (under-)fitting does not occur. This is demonstrated in simulations and data examples and is supported by theoretical investigations."],["dc.identifier.doi","10.1198/016214507000000978"],["dc.identifier.gro","3145861"],["dc.identifier.pmid","1630949"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3592"],["dc.language.iso","en"],["dc.notes.intern","mathe"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0162-1459"],["dc.subject","correlation structure misspecification smoothing parameter selection linear mixed model"],["dc.title","A Note on Penalized Spline Smoothing With Correlated Errors"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","487"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society: Series B (Statistical Methodology)"],["dc.bibliographiccitation.lastpage","503"],["dc.bibliographiccitation.volume","71"],["dc.contributor.author","Kauermann, Göran"],["dc.contributor.author","Krivobokova, Tatyana"],["dc.contributor.author","Fahrmeir, Ludwig"],["dc.date.accessioned","2017-09-07T11:50:09Z"],["dc.date.available","2017-09-07T11:50:09Z"],["dc.date.issued","2009"],["dc.description.abstract","The paper discusses asymptotic properties of penalized spline smoothing if the spline basis increases with the sample size. The proof is provided in a generalized smoothing model allowing for non-normal responses. The results are extended in two ways. First, assuming the spline coefficients to be a priori normally distributed links the smoothing framework to generalized linear mixed models. We consider the asymptotic rates such that the Laplace approximation is justified and the resulting fits in the mixed model correspond to penalized spline estimates. Secondly, we make use of a fully Bayesian viewpoint by imposing an a priori distribution on all parameters and coefficients. We argue that with the postulated rates at which the spline basis dimension increases with the sample size the posterior distribution of the spline coefficients is approximately normal. The validity of this result is investigated in finite samples by comparing Markov chain Monte Carlo results with their asymptotic approximation in a simulation study."],["dc.identifier.doi","10.1111/j.1467-9868.2008.00691.x"],["dc.identifier.gro","3145859"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3589"],["dc.language.iso","en"],["dc.notes.intern","mathe"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1369-7412"],["dc.subject","Bayesian splines Laplace approximation Mixed models Penalized quasi-likelihood Penalized splines Smoothing"],["dc.title","Some asymptotic results on generalized penalized spline smoothing"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]