Competing Risk Models

Caesarian Section (CS) rates have been known to have geographical varaitions. The purpose of this paper was to determine Ghana's situation (regional trend) and also to provide a two-year forcast estimates for the ten (10) regions of Ghana. The data was longitudinal and comprised monthly CS records of women from 2008 to 2017. The dataset was divided into training and testing dataset. A total of eighty four (84) months were used as the training dataset and the remaining thirty six (36) months were used as testing dataset. The ARIMA methodology was applied in the analysis. Augmented Dicker-Fuller (ADF), KPSS and the Philips-Perron (PP) unit root tests were employed to test for stationarity of the series plot. KPSS (which is known to give more robust results) and PP test consistently showed that the series was stationary (p ＜ 0.05) for all ten (10) regions, although there were some conflicting results with the ADF test for some regions. Tentative models were formulated for each region and the model with the lowest AIC was selected as the "Best" model fit for respective regions of Ghana. The "best" Model fit for Greater Accra, Central and Eastern regions were respectively SARIMA (2, 0, 0) (0, 1, 1) 12 , SARIMA (2, 0, 0) (0, 1, 1) 12 with a Drift and SARIMA (1, 1, 1) (0, 1, 1) 12. Additionally, the best model fit for Northern and Volta regions were SARIMA (3,0,2) (0,1,1) 12 with drift and SARIMA (0,1,1) (0,1,1) 12. Ashanti, Upper East and Western regions failed the JB test or the normality test for the residuals. Upper West and Brong Ahafo Regions were not suitable for forecasting due failure to depict white noise and ARCH test failure, respectively. The best models fit were used to forecast for 2019 and 2020. The results showed that regional variations of CS exist in Ghana. The study recommended for future studies to apply methods that will allow for forecasting for regions which failed the test under the methods used in this study.

The second Risk Management International Workshop, “Risk Information Management, Risk Models and Applications, 2014,” with special emphasis on risk information management, was held in Berlin November 17–18, 2014. The workshop facilitated an extensive discussion on sharing best practices in multidisciplinary risk modeling, risk information management, risk model applications, implementation, integration on an interdisciplinary level, and harmonization of interdisciplinary risk information. These applications covered a wide diversity of fields ranging from natural hazards like flood and climate risks, technical risks, aviation risks with drones, health risks, costs of natural hazards, social vulnerability, cloud computing and social media, to financial risk management applications. A special session offered expertise in Business Process Modeling, a substantial extension to currently widespread service structure implementation and use.

State owned enterprises (SOEs) in China have undergone significant restructuring since the mid-1990s. To date, scholars have devoted considerable attention to the constraints and motives of corporate restructuring in China. Yet the majority of the existing studies treat restructuring as a simple ownership transfer from the state to non-state entities without differentiating the resulting ownership structure of the firm. Consequently, we know relatively little about why otherwise similar SOEs were restructured at different times and through different means. This study intends to fill this gap by examining the determinants of both the timing and methods of restructuring using a unique longitudinal survey of 145 SOEs over an eleven-year period. Findings of this study will shed new light on the economic and political logic of corporate restructuring in China.

In the study of multiple decrement lifetimes one considers a group of K lives. The group is said to survive at least as long as all members are living. In practise, the component lives are usually combined in annuity or insurance, or some other common undertaking, and frequently are related by blood, marriage or some joint undertaking which simultaneously exposes the individuals to common hazards and mortality risks. It follows that in general the joint survival function of the group should exhibit dependence between the component lives. The method of analysis now commonly used for multiple decrement analysis however assumes independence of the joint lives and joint life insurance payable upon the first death in the group is calculated using this assumption (cf. Jordan, 1975, Chapter 9).