Forschungsseminar Mathematische Statistik
Für den Bereich Statistik
A. Carpentier, S. Greven, W. Härdle, M. Reiß, V. Spokoiny
Ort
Weierstrass-Institut für Angewandte Analysis und Stochastik
Erhard-Schmidt-Raum
Mohrenstrasse 39
10117 Berlin
Zeit
mittwochs, 10.00 - 12.00 Uhr
Programm
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- 19. April 2023
- Chen Huang (University of Aarhus)
- Arellano-Bond LASSO Estimator for Long Panel Dynamic Linear Models
- Abstract: We consider the estimation and inference for dynamic linear panel models with both large cross-sectional dimension $N$ and long time dimension $T$. The Arellano-Bond (AB) estimator (Arellano and Bond, 1991) is a popular method for dynamic panels. However, it has large bias when $T$ is large. We present a simple least absolute shrinkage and selection operator (LASSO) estimator for the AB method. In particular, we first use LASSO to fit the optimal instrument variables (IV) based on a large group of lags, and then implement the linear IV estimator in the second stage. To further reduce the bias of the estimator, we propose a sample-splitting procedure. Simulations show that the sample-splitting AB-LASSO provides more accuracy in estimation and inference compared to the AB method. Finally, we apply the approach to evaluate the long run effect of democracy on the economic growth.
- 26. April 2023
- N.N.
- 03. Mai 2023
- N.N.
- 10. Mai 2023
- N.N.
- 17. Mai 2023
- Enno Mammen (Mathematicon Universität Heidelberg)
- Random Planted Forest: a directly interpretable tree ensemble
- Abstract: We introduce a novel interpretable, tree based algorithm for prediction in a regression setting in which each tree in a classical random forest is replaced by a family of planted trees that grow simultaneously. The motivation for our algorithm is to estimate the unknown regression function from a functional ANOVA decomposition perspective, where each tree corresponds to a function within that decomposition. Therefore, planted trees are limited in the number of interaction terms. The maximal order of approximation in the ANOVA decomposition can be specified or left unlimited. If a first order approximation is chosen, the result is an additive model. In the other extreme case, if the order of approximation is not limited, the resulting model places no restrictions on the form of the regression function. The talk reports on joint work with Munir Hiabu and Joseph T. Meyer.
- 24. Mai 2023
- Jonathan Niles-Weed (Courant Institute)
- tba
- 31. Mai 2023
- N.N.
- 07. Juni 2023
- N.N.
- 14. Juni 2023
- N.N.
- 21. Juni 2023
- N.N.
- 28. Juni 2023
- Alessia Caponera (Universität Milano-Bicocca)
- tba
- Kartik Waghmare (EPFL Lausanne)
- tba
- 05. Juli 2023
- N.N.
- 21. Juli 2023
- N.N.
- 19. Juli 2023
- N.N.
Interessenten sind herzlich eingeladen.
Für Rückfragen wenden Sie sich bitte an:
Frau Andrea Fiebig
Mail: fiebig@mathematik.hu-berlin.de
Telefon: +49-30-2093-45460
Fax: +49-30-2093-45451
Humboldt-Universität zu Berlin
Institut für Mathematik
Unter den Linden 6
10099 Berlin, Germany