dc.contributor.advisor | Krivobokova, Tatyana Prof. Dr. | |
dc.contributor.author | Singer, Marco | |
dc.date.accessioned | 2016-09-12T09:47:33Z | |
dc.date.available | 2016-09-12T09:47:33Z | |
dc.date.issued | 2016-09-12 | |
dc.identifier.uri | http://hdl.handle.net/11858/00-1735-0000-0028-8831-B | |
dc.identifier.uri | http://dx.doi.org/10.53846/goediss-5828 | |
dc.language.iso | deu | de |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject.ddc | 510 | de |
dc.title | Partial Least Squares for Serially Dependent Data | de |
dc.type | doctoralThesis | de |
dc.contributor.referee | Krivobokova, Tatyana Prof. Dr. | |
dc.date.examination | 2016-08-04 | |
dc.description.abstracteng | In the first paper we consider the partial least squares algorithm for dependent data and study the consequences
of ignoring the dependence both theoretically and numerically. Ignoring nonstationary dependence
structures can lead to inconsistent estimation, but a simple modification leads to consistent
estimation. A protein dynamics example illustrates the superior predictive power of the
method. For the second paper we consider the kernel partial least squares algorithm for the solution of nonparametric regression
problems when the data exhibit dependence in their observations in the form of stationary
time series. Probabilistic convergence rates of the kernel partial least squares estimator to the
true regression function are established under a source condition. The impact of long range
dependence in the data is studied both theoretically and in simulations. | de |
dc.contributor.coReferee | Munk, Axel Prof. Dr. | |
dc.subject.eng | Dependent data, Kernel partial least squares, Latent variable model, Long range dependence, Nonparametric regression, Nonstationary process, Partial least squares, Protein dynamics, Source condition, Stationary process | de |
dc.identifier.urn | urn:nbn:de:gbv:7-11858/00-1735-0000-0028-8831-B-2 | |
dc.affiliation.institute | Fakultät für Mathematik und Informatik | de |
dc.subject.gokfull | Mathematik (PPN61756535X) | de |
dc.identifier.ppn | 869469967 | |