Nonparametric data reduction approach for large-scale survival data analysis

In the era of big data, analysis of complex and huge data expends time and money, may cause errors and misinterpretations. Consequently, inaccurate and erroneous reasoning could lead to poor inference and decision making, sometimes irreversible and catastrophic events. On the other hand, proper management and utilization of valuable data could significantly increase knowledge and reduce cost by preventive actions. In this field, time-to-event and survival data analysis is a kernel of risk assessment and have an inevitable role in predicting the probability of many events occurrence such as failure of a device or component. Thus, in the presence of large-scale, massive and complex data, specifically in terms of variables, applying proper methods to efficiently simplify such data before any analysis process is desired. In this paper we propose an applied data reduction approach which enables us to obtain appropriate variable selection in high dimensional and large-scale data in order to avoid aforementioned difficulties in decision making and facilitate survival data and failure analysis. This paper present applied data reduction and variable selection approach for risk assessment and decision making in complex large-scale survival data analysis.