On Rank Estimators in Increasing Dimensions
报告人： Yanqin Fan (University of Washington)
The family of rank estimators, including Han's maximum rank correlation estimator (Han, 1987) as a notable example, has been widely exploited in studying regression problems. For these estimators,
although the linear index is introduced for alleviating the impact of dimensionality, the effect of large dimension on inference is rarely studied. This paper fills this gap via studying the statistical properties of a larger family of M-estimators, whose objective functions are formulated as U-processes and may be discontinuous in increasing dimension set-up where the number of parameters, in the model is allowed to increase with the sample size. All theoretical results are further backed up by simulation studies.
About the Speaker:
Yanqin Fan is a Castor Professor of Economics at University of Washington. Her recent research focuses on partial identification of distributional treatment effects and uniform inference, inequality-constrained estimation and inference, estimation and inference in increasing dimension, and modal regression. She has published her work in leading journals in economics/econometrics/statistics including Econometrica, Review of Economic Studies, Journal of the American Statistical Association, Journal of Econometrics, and Econometric Theory. She is currently serving as AE for several journals including Journal of Econometrics, Journal of Financial Econometrics, and Econometric Reviews.