Parameter estimation with non-ignorable missing data is a challenging problem in statistics. The fully parametric approach for joint modeling of the response model and the population model can produce results that are quite sensitive to the failure of the assumed model. We propose a more robust modeling approach by considering the model for the nonresponding part as an exponential tilting of th...

The authors consider the problem of Bayesian variable selection for proportional hazards regression models with right censored data. They propose a semi-parametric approach in which a nonparametric prior is specified for the baseline hazard rate and a fully parametric prior is specified for the regression coefficients. For the baseline hazard, they use a discrete gamma process prior, and for th...

Information derived from interim sacrifices or on cause-of-death is routinely used in the statistical analyses of carcinogenicity experiments involving occult tumours. In this article, the authors describe a simple semi-parametric model which does not require this information. Natural deaths during the experiment and the usual terminal sacrifice provide sufficient information to ensure that the...

Linear regression models are widely used in mental health and related health services research. However, the classic linear regression analysis assumes that the data are normally distributed, an assumption that is not met by the data obtained in many studies. One method of dealing with this problem is to use semi-parametric models, which do not require that the data be normally distributed. But...

Abstract. In this study we used the parametric survival approach to analyze the survival time of African American breast cancer patients. In research about the survival time of patients, the Cox Proportional Hazard model, a semiparametric method, is primarily used, but this approach does not rely on the distributional assumptions. The parametric method is more consistent with a theoretical appr...

Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events that can be non-exponentially distributed. Within parametric ACTMCs, the parameters of alarm-event distributions are not given explicitly and can be subject of parameter synthesis. An algorithm solving the ε-optimal parameter synthesis problem for parametric ACTMCs with long-run average optimization objectives is presente...