A0546
Title: One sample test for high-dimensional mean with missing data based on $l_2$ norm
Authors: Shiv Kumar Yadav - Indian Institute of Technology Bombay (India) [presenting]
Abstract: The purpose is to address the challenges of testing the mean vector in high-dimensional populations with missing data. Building on a recent study, the asymptotic normality of the test statistic is established under weaker moment conditions, allowing for moments up to order $( 2+\delta )$ for $( \delta \in (0, 2) )$. This relaxation is crucial for processes like GARCH models, where higher-order moments may not exist. The method, combining truncation with a moment-based approach, handles missing data and extends the test's applicability to geometric $( \alpha )$-mixing processes, offering a more flexible framework for high-dimensional statistical analysis.
