Print Email Facebook Twitter Bayesian Nonparametric Estimation with Shape Constraints Title Bayesian Nonparametric Estimation with Shape Constraints Author Pang, L. (TU Delft Statistics) Contributor Jongbloed, G. (promotor) van der Meulen, F.H. (copromotor) Degree granting institution Delft University of Technology Date 2020-08-28 Abstract This thesis deals with a number of statistical problems where either censoringor shape-constraints play a role. These problems have mostly been treated from a frequentist statistical perspective. Over the past decades, the Bayesian approachto statistics has gained popularity and this is the approach that is adopted in thisthesis. We consider nonparametric statistical models, i.e. models indexed by a parameter that is not of finite dimension. For three different models we investigate the asymptotic properties of the posterior distribution under a frequentist setup. We derive either posterior consistency or posterior contraction rat es. Such results are relevant, as these provides a frequentist justification of using point estimators derived from the posterior. Besides theoretical results, we develop computational methods for obtaining draws from the posterior. Overall, this work is at the intersection of the research areas "estimation under shape constraints and censoring", "Bayesian nonparametrics" and "Bayesian computation". To reference this document use: https://doi.org/10.4233/uuid:cde75cae-91c8-4f4d-9b65-51bee023cd08 Part of collection Institutional Repository Document type doctoral thesis Rights © 2020 L. Pang Files PDF thesis_lixuepang.pdf 1.65 MB Close viewer /islandora/object/uuid:cde75cae-91c8-4f4d-9b65-51bee023cd08/datastream/OBJ/view