Modeling China's Per Capita Disposable Income by Uncertain Statistics

Abstract

Uncertain statistics is a set of mathematical techniques for collecting, analyzing and interpreting data by uncertainty theory. There are mainly three modeling methods in uncertain statistics: uncertain time series analysis, uncertain regression analysis, and uncertain differential equation. This paper applies these tools to modeling China's per capita disposable income, and employs uncertain hypothesis test to determine whether the estimated uncertain statistical models fit China's per capita disposable income. In addition, this paper shows that it is necessary to use uncertain statistics instead of probability statistics to model China's per capita disposable income by investigating the corresponding residuals.