Abstract: In arenas of application including environmental science, economics, and medicine, it is increasingly common to consider time series of curves or functions. Many inferential procedures employed in the analysis of such data involve the long-run covariance function, which is analogous to the long-run covariance matrix familiar to finite-dimensional time series analysis and econometrics. I present a kernel sandwich estimator for estimating the long-run covariance. From estimated long-run covariance, I study the estimation of a long-memory parameter in a long-range dependent stationary functional time series, and identify the most accurate estimation method via a series of simulation studies.
Biography: Han Lin Shang is an Associate Professor of Statistics at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research interests include actuarial studies, computational statistics, demographic forecasting and empirical finance. He is serving as an associate editor for Journal of Computational and Graphical Statistics and Australian & New Zealand Journal of Statistics.