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pro vyhledávání: '"sparse estimation"'
We introduce a novel Bayesian approach for both covariate selection and sparse precision matrix estimation in the context of high-dimensional Gaussian graphical models involving multiple responses. Our approach provides a sparse estimation of the thr
Externí odkaz:
http://arxiv.org/abs/2409.16276
Publikováno v:
ICML2024 Proceedings
Previous studies yielded discouraging results for item-level locally differentially private linear regression with $s^*$-sparsity assumption, where the minimax rate for $nm$ samples is $\mathcal{O}(s^{*}d / nm\varepsilon^2)$. This can be challenging
Externí odkaz:
http://arxiv.org/abs/2408.04313
We study Gaussian sparse estimation tasks in Huber's contamination model with a focus on mean estimation, PCA, and linear regression. For each of these tasks, we give the first sample and computationally efficient robust estimators with optimal error
Externí odkaz:
http://arxiv.org/abs/2403.10416
Autor:
Qi, Hangtian1 (AUTHOR) qht20@mails.tsinghua.edu.cn, Cui, Xiaowei1 (AUTHOR) cxw2005@tsinghua.edu.cn, Lu, Mingquan1,2 (AUTHOR) lumq@tsinghua.edu.cn
Publikováno v:
Remote Sensing. Sep2024, Vol. 16 Issue 18, p3537. 24p.
Extremely large-scale antenna array (ELAA) is promising as one of the key ingredients for the sixth generation (6G) of wireless communications. The electromagnetic propagation of spherical wavefronts introduces an additional distance-dependent dimens
Externí odkaz:
http://arxiv.org/abs/2403.12506
Autor:
Suzuki, Joe
Publikováno v:
The Journal of the Japan Statistical Society, Volume 53, Issue 1, September 2023 (pages 139-167) (in Japanese)
When the model is not known and parameter testing or interval estimation is conducted after model selection, it is necessary to consider selective inference. This paper discusses this issue in the context of sparse estimation. Firstly, we describe se
Externí odkaz:
http://arxiv.org/abs/2310.05685
Akademický článek
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Autor:
Kusano, Shogo, Uchida, Masayuki
We consider structural equation modeling (SEM) with latent variables for diffusion processes based on high-frequency data. The quasi-likelihood estimators for parameters in the SEM are proposed. The goodness-of-fit test is derived from the quasi-like
Externí odkaz:
http://arxiv.org/abs/2305.02655
The central problem we address in this work is estimation of the parameter support set S, the set of indices corresponding to nonzero parameters, in the context of a sparse parametric likelihood model for count-valued multivariate time series. We dev
Externí odkaz:
http://arxiv.org/abs/2307.09684