HIGH DIMENSIONAL TIME SERIES ANALYSIS AND ITS APPLICATION IN MODELING TRANSMISSION DYNAMICS OF DENGUE

The testing of white noise (WN) is an essential step in time series analysis. In a high dimensional set-up, most existing methods either are computationally infeasible, or suffer from highly distorted Type-I errors, or both. To address this problem, we propose an easy-to-implement bootstrap method for high-dimensional WN test and prove its consistency for a variety of test statistics. Its power properties as well as extensions to WN tests based on fitted residuals are also considered. Simulation results show that compared to the existing methods, the new approach possesses much higher power, while maintaining a proper control over the Type-I error. Furthermore, we apply the method in time series analysis to discover the underlying dynamics of dengue, which has an estimated 390 million infections occur around the world. By incorporating the weather conditions in a time-series-susceptible infectious-recovered model with newly proposed all-step-ahead fitting approach, we have succeeded in reproducing the dengue dynamics. The proposed model can statistically justify the significance of environmental factors on dengue transmission, thus providing deeper insight into the transmission and addressing several epidemiological puzzles.

Author dc:creator

• WANG LENGYANG

• SIR model
• infectious disease
• mean test
• white noise test
• high dimensional data
• time series