Forschungsgruppe "Stochastische Algorithmen und Nichtparametrische Statistik"
Research Seminar "Mathematical Statistics" Winter Semester 2024/25
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| 16.10.2024 | Botond Szabo (Bocconi Milan) |
| HVP 11 a, R.313 | Privacy constrained semiparametric inference
For semi-parametric problems differential private estimators are typically constructed in a case-by-case basis. In this work we develop a privacy constrained semi-parametric plug-in approach, which can be used in general, over a collection of semi-parametric problems. We derive minimax lower and matching upper bounds for this approach and provide an adaptive procedure in case of irregular (atomic) functionals. Joint work with Lukas Steinberger (Vienna) and Thibault Randrianarisoa (Toronto, Vector Institute). |
| 23.10.2024 | Prof. Dr. Weining Wang (University of Groningen) |
| HVP 11 a, R.313 | Conditional nonparametric variable screening by neural factor regression High-dimensional covariates often admit linear factor structure. To effectively screen correlated covariates in high-dimension, we propose a conditional variable screening test based on non-parametric regression using neural networks due to their representation power. We ask the question whether individual covariates have additional contributions given the latent factors or more generally a set of variables. Our test statistics are based on the estimated partial derivative of the regression function of the candidate variable for screening and a observable proxy for the latent factors. Hence, our test reveals how much predictors contribute additionally to the non-parametric regression after accounting for the latent factors. Our derivative estimator is the convolution of a deep neural network regression estimator and a smoothing kernel. We demonstrate that when the neural network size diverges with the sample size, unlike estimating the regression function itself, it is necessary to smooth the partial derivative of the neural network estimator to recover the desired convergence rate for the derivative. Moreover, our screening test achieves asymptotic normality under the null after finely centering our test statistics that makes the biases negligible, as well as consistency for local alternatives under mild conditions. We demonstrate the performance of our test in a simulation study and two real world applications. |
| 30.10.2024 | Olga Klopp (ESSEC Business School, Paris) |
| HVP 11 a, R.313 | Adaptive density estimation under low-rank constraints In this talk, we address the challenge of bivariate probability density estimation under low-rank constraints for both discrete and continuous distributions. For discrete distributions, we model the target as a low-rank probability matrix. In the continuous case, we assume the density function is Lipschitz continuous over an unknown compact rectangular support and can be decomposed into a sum of K separable components, each represented as a product of two one-dimensional functions. We introduce an estimator that leverages these low-rank constraints, achieving significantly improved convergence rates. We also derive lower bounds for both discrete and continuous cases, demonstrating that our estimators achieve minimax optimal convergence rates within logarithmic factors. |
| 06.11.2024 | Prof. Dr. Vladimir Spokoiny (WIAS und HU Berlin) | HVP 11 a, R.313 | Regression estimation and inference in high dimension The talk discusses a general non-asymptotic and non-minimax approach to statistical estimation and inference with applications to nonlinear regression. The main results provide finite-sample Fisher and Wilks expansions for the maximum-likelihood estimator with an explicit remainder in terms of the effective dimension of the problem. |
| 13.11.2024 | Workshop: Foundations of Modern Nonparametric Statistics |
| Findet in Adlershof statt !!! | |
| 20.11.2024 | Bernhard Stankewitz (Universität Potsdam) |
| HVP 11 a, R.313 | Contraction rates for conjugate gradient
and Lanczos approximate posteriors in Gaussian process regression
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| 27.11.2024 | Julien Chhor (Toulouse School of Economics) |
| HVP 11 a, R.313 | Locally sharp goodness-of-fit testing in sup norm for high-dimensional counts
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| 04.12.2024 | |
| HVP 11 a, R.313 | |
| 11.12.2024 | |
| HVP 11 a, R.313 | |
| 18.12.2024 | |
| HVP 11 a, R.313 | |
| 08.01.2025 | |
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| 15.01.2025 | |
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| 22.01.2025 | |
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| 29.01.2024 | |
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last reviewed: October 8, 2024 by Christine Schneider