Modern Engineering Asset Management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Suitable mathematical models that are capable of predicting Time-to-Failure (TTF) and the probability of failure in future time are essential. In traditional...

Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly in financial investment and lending. The potential value of such models has been recently emphasised by the extremely costly failure of high profile businesses in both Australia and...

An important goal of research involving gene expression data for outcome prediction is to establish the ability of genomic data to define clinically relevant risk factors. Recent studies have demonstrated that microarray data can successfully cluster patients into low and high risk categories....

Dependent censoring occurs in longitudinal studies of recurrent events when the censoring time depends on the potentially unobserved recurrent event times. To perform regression analysis in this setting, we propose a semiparametric joint model that formulates the marginal distributions of the...

Cluster randomized trials, in which social units are selected as the units ofrandomization, have been increasingly used in the past three decades to evaluatethe effects of intervention. This thesis is devoted to design and analysis of clusterrandomized trials.Regarding design, we introduce a new...

This research deals with some statistical modeling problems that are motivated by credit risk analysis. Credit risk modeling has been the subject of considerable research interest in finance and has recently drawn the attention of statistical researchers. In the first chapter, we provide an...

We study unrepresentative observational data in survival analysis. The first paper focuses on proportional hazards regressionwhen observed subjects have different selection probabilities. Wedevelop methods which are applicable when the selectionprobabilities are unknown but estimated using...

In tumorigenicity experiments, a complication is that the time to event is generally not observed, so that the time to tumor is subject to interval censoring. One of the goals in these studies is to properly model the effect of dose on risk. Thus, it is important to have goodness of fit...

The research in this thesis focuses on methods for estimating the cumulative treatment effect on time to an event in the setting when the treatment-specific hazards are not proportional. In clinical studies of time to event data, non-proportional hazards are very common. The Cox model is...

In this article we investigate regression calibration methods to jointly model longitudinal and survival data using a semiparametric longitudinal model and a proportional hazards model. In the longitudinal model, a biomarker is assumed to follow a semiparametric mixed model where covariate...