Nonparametric Heterogeneity Testing For Massive Data

Understand

A massive dataset often consists of a growing number of (potentially) heterogeneous sub-populations.

• This paper is concerned about testing various forms of heterogeneity arising from massive data.
• In a general nonparametric framework, a set of testing procedures are designed to accommodate a growing number of sub-populations, denoted as $s$, with computational feasibility.
• In theory, their null limit distributions are derived as being nearly Chi-square with diverging degrees of freedom as long as $s$ does not grow too fast.