A0981
Title: Fixed-accuracy big data estimation of population Gini income inequality index: Practical distribution-free strategies
Authors: Nitis Mukhopadhyay - University of Connecticut-Storrs (United States) [presenting]
Abstract: Recently, elegant sequential fixed-width confidence interval (FWCI) and minimum risk point estimation (MRPE) methodologies for $G(F)$ have been developed. $G(F)$ is the celebrated Gini income inequality index in a population associated with an unknown distribution function $F$ having its support on positive real numbers. We revisit both problems from the vantage point of big data science by proposing newly designed easy-to-implement sequential estimation strategies with nearly minimal computational complexities and technical difficulties. Inference techniques recently introduced will be emphasized. We show that these new sequential estimation strategies have a wide range of appealing asymptotic properties including both first-order and second-order approximations. The proposed approaches are flexible enough to embrace other non-standard inference problems in the future.
