Econometrics Seminar Series. "Least Trimmed Squares: Nuisance Parameter Free Asymptotics" (Joint Work with Bent Nielsen)
Published: 9 February 2025
14 March 2025. Dr Vanessa Berenguer-Rico, University of Oxford
Associate Professor Vanessa Berenguer-Rico, University of Oxford
"Least Trimmed Squares: Nuisance Parameter Free Asymptotics" (Joint Work with Bent Nielsen)
Friday, 14 March 2025. 15:00-16:30
Room 141A, Adam Smith Business School Building
Abstract
The Least Trimmed Squares (LTS) regression estimator is known to be very robust to the presence of ‘outliers’. It is based on a clear and intuitive idea: in a sample of size n, it searches for the h-subsample of observations with the smallest sum of squared residuals. The remaining n−h observations are declared ‘outliers’. Fast algorithms for its computation exist. Nevertheless, the existing asymptotic theory for LTS, based on the traditional $\epsilon$-contamination model, shows that the asymptotic behaviour of both regression and scale estimators depend on nuisance parameters. Using a recently proposed new model, in which the LTS estimator is maximum likelihood, we show that the asymptotic behaviour of both the LTS regression and scale estimators are free of nuisance parameters. Thus, with the new model as a benchmark, standard inference procedures apply while allowing a broad range of contamination.
Bio
Vanessa is an Associate Professor of Economics at the University of Oxford. She is an Econometrician and her most recent work has focused on robust statistics. She has also worked on time series analysis. She studied her undergraduate degree in Economics at the University of Barcelona and her PhD at the Universidad Carlos III de Madrid.
For further information, please contact business-seminar-series@glasgow.ac.uk.
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First published: 9 February 2025