Assume we observe a finite number of inspection times together with information on whether a specific event has occurred before each of these times. Suppose replicated measurements are available on multiple event times. The set of inspection times, including the number of inspections, may be different for each event. This is known as mixed case interval censored data. We consider Bayesian estimation of the distribution function of the event time while assuming it is concave. We provide sufficient conditions on the prior such that the resulting procedure is consistent from the Bayesian point of view. We also provide computational methods for drawing from the posterior and illustrate the performance of the Bayesian method in both a simulation study and two real datasets.

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Suppose X₁, …, X_n is a random sample from a bounded and decreasing density f₀ on [0, ∞). We are interested in estimating such f₀, with special interest in f₀ (0). This problem is encountered in various statistical applications and has gained quite some attention in the statistical literature. It is well known that the maximum likelihood estimator is inconsistent at zero. This has led several authors to propose alternative estimators which are consistent. As any decreasing density can be represented as a scale mixture of uniform densities, a Bayesian estimator is obtained by endowing the mixture distribution with the Dirichlet process prior. Assuming this prior, we derive contraction rates of the posterior density at zero by carefully revising arguments presented in Salomond (Electronic Journal of Statistics 8 (2014) 1380– 1404). Several choices of base measure are numerically evaluated and compared. In a simulation various frequentist methods and a Bayesian estimator are compared. Finally, the Bayesian procedure is applied to current durations data described in Slama et al. (Human Reproduction 27 (2012) 1489–1498).

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We consider the current status continuous mark model where, if an event takes place before an inspection time T a “continuous mark” variable is observed as well. A Bayesian nonparametric method is introduced for estimating the distribution function of the joint distribution of the event time (X) and mark variable (Y). We consider two histogram-type priors on the density of (Formula presented.). Our main result shows that under appropriate conditions, the posterior distribution function contracts pointwisely at rate (Formula presented.) if the true density is (Formula presented.) -Hölder continuous. In addition to our theoretical results we provide efficient computational methods for drawing from the posterior relying on a noncentered parameterization and Crank–Nicolson updates. The performance of the proposed methods is illustrated in several numerical experiments.

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This thesis deals with a number of statistical problems where either censoring or shape-constraints play a role. These problems have mostly been treated from a frequentist statistical perspective. Over the past decades, the Bayesian approach to statistics has gained popularity and this is the approach that is adopted in this thesis. 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".@en