Graduate Theses & Dissertations

Long-term Financial Sustainability of China's Urban Basic Pension System
Population aging has become a worldwide concern since the nineteenth century. The decrease in birth rate and the increase in life expectancy will make China’s population age rapidly. If the growth rate of the number of workers is less than that of the number of retirees, in the long run, there will be fewer workers per retiree. This will apply great pressure to China’s public pension system in the next several decades. This is a global problem known as the “pension crisis”. In this thesis, a long-term vision for China’s urban pension system is presented. Based on the mathematical models and the projections for demographic variables, economic variables and pension scheme variables, we test how the changes in key variables affect the balances of the pension fund in the next 27 years. This thesis applies methods of deterministic and stochastic modeling as well as sensitivity analysis to the problem. Using sensitivity analysis, we find that the pension fund balance is highly sensitive to the changes in retirement age compared with other key variables. Monte Carlo simulations are also used to find the possible distributions of the pension fund balance by the end of the projection period. Finally, according to my analysis, several changes in retirement age are recommended in order to maintain the sustainability of China’s urban basic pension scheme. Author Keywords: China, demographic changes, Monte Carlo simulation, pension fund, sensitivity tests, sustainability
Smote and Performance Measures for Machine Learning Applied to Real-Time Bidding
In the context of Real-Time Bidding (RTB) the machine learning problems of imbalanced classes and model selection are investigated. Synthetic Minority Oversampling Technique (SMOTE) is commonly used to combat imbalanced classes but a shortcoming is identified. Use of a distance threshold is identified as a solution and testing in a live RTB environment shows significant improvement. For model selection, the statistical measure Critical Success Index (CSI) is modified to add emphasis on recall. This new measure (CSI-R) is empirically compared with other measures such as accuracy, lift, efficiency, true skill score, Heidke's skill score and Gilbert's skill score. In all cases CSI-R is shown to provide better application to the RTB industry. Author Keywords: imbalanced classes, machine learning, online advertising, performance measures, real-time bidding, SMOTE

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