Revised Blog Assignment

Article 1.

-Reference:

Yingsaeree, C., Nuti, G., & Treleaven, P. (2010). Computational finance. Computer, 43(12), 36-43.

The authors notified that computational finance consists of two branches, data mining and computer modeling. The authors listed some tools, methods and financial forecasting as applications. Moreover, Yingsaeree, C., Nuti, G., & Treleaven, P. (2010) noted that in parametric optimization “if the problem considered has a linear objective function and linear constraints, stat­isticians can use numerical analysis techniques such as interior-point and simplex methods. However, when the objective function and constraints are nonlinear, statisti­cians frequently use stochastic search algorithms derived from AI, such as genetic algorithms and simulated anneal­ing, to obtain an approximation of the optimal answer” (p. 39).

Article 2.

-Reference:

Zhou, S., & Zhu, N. (2013). Multiple regression models for energy consumption of office buildings in different climates in china.Frontiers in Energy, 7(1), 103-110.

Different combinations of the 8 key influencing factors were simulated in Trnsys and the regression models were proved to be feasible and accurate to estimate the Trnsys simulation. Besides, the multiple regression models were established respectively in the four climates in China based on the simulated results. Most importantly, the error rates always remain within +/-15%. The error rates in actual case evaluations seem to be larger than those in simulation evaluations. This can be explained by the fact that the regression models were established on the basis of the typical meteorological year data while the actual energy consumption was recorded in a specific year (Zhou, S., & Zhu, N., 2013, p. 109).

Article 3.

-Reference:

Chen, V. C., Stewart, R., & Lee, C. T. (2012). Weekly lottery sales volume and suicide numbers: A time series analysis on national data from Taiwan. Social Psychiatry and Psychiatric Epidemiology, 47(7), 1055-1059.

In a time-series analysis of national data, the authors investigated associations between weekly suicide numbers and weekly lottery sales volumes, finding a positive association with sales for one lottery system (higher cost, higher winnings, lower probability of winning) but a negative association with another (lower cost, lower winnings, higher probability of winning). Both associations were strongest for male compared to female suicides. The strengths of the design include the large and representative samples and potentially high level of statistical power. The principal limitation with any study of this nature is that causality is difficult to infer and we can only say that findings are consistent with a given hypothesis (Chen, V. C., Stewart, R., & Lee, C. T., 2012, p. 1058).