Optimal and Robust Designs of Step-stress Accelerated Life Testing Experiments for Proportional Hazards Models

Accelerated life testing (ALT) is widely used to obtain reliability information about a product within a limited time frame. The Cox s proportional hazards (PH) model is often utilized for reliability prediction. My master thesis research focuses on designing accelerated life testing experiments for reliability estimation. We consider multiple step-stress ALT plans with censoring. The optimal stress levels and times of changing the stress levels are investigated. We discuss the optimal designs under three optimality criteria. They are D-, A- and Q-optimal designs. We note that the classical designs are optimal only if the model assumed is correct. Due to the nature of prediction made from ALT experimental data, attained under the stress levels higher than the normal condition, extrapolation is encountered. In such case, the assumed model cannot be tested. Therefore, for possible imprecision in the assumed PH model, the method of construction for robust designs is also explored.

The role of intrapartum intravenous therapy and method of delivery on newborn weight loss : challenging the 7% rule

It is common practice to initiate supplemental feeding in newborns if body weight decreases by 7-10% in the first few days after birth (7-10% rule). Standard hospital procedure is to initiate intravenous therapy once a woman is admitted to give birth. However, little is known about the relationship between intrapartum intravenous therapy and the amount of weight loss in the newborn. The present research was undertaken in order to determine what factors contribute to weight loss in a newborn, and to examine the relationship between the practice of intravenous intrapartum therapy and the extent of weight loss post-birth. Using a cross-sectional design with a systematic random sample of 100 mother-baby dyads, we examined properties of delivery that have the potential to impact weight loss in the newborn, including method of delivery, parity, duration of labour, volume of intravenous therapy, feeding method, and birth attendant. This study indicated that the volume of intravenous therapy and method of delivery are significant predictors of weight loss in the newborn (R2=15.5, p<0.01). ROC curve analysis identified an intravenous volume cut-point of 1225 ml that would elicit a high measure of sensitivity (91.3%), and demonstrated significant Kappa agreement (p<0.01) with excess newborn weight loss. It was concluded that infusion of intravenous therapy and natural birth delivery are discriminant factors that influence excess weight loss in newborn infants. Acknowledgement of these factors should be considered in clinical practice.

Using a Bayesian model for bankruptcy prediction : a comparative approach

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.