Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Liu, Catherine C."'
Autor:
Li, Yang, Deng, Jianing, Zhong, Chong, Yang, Danjuan, Li, Meiyan, Welsh, A. H., Liu, Aiyi, Zhou, Xingtao, Liu, Catherine C., Fu, Bo
Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging and joint modeling of multiple discrete and continuous clinical scores presents a promising new paradigm for multi-task problems in Ophthalmology. The bi-channel framework that
Externí odkaz:
http://arxiv.org/abs/2408.09395
Autor:
Zhong, Chong, Li, Yang, Yang, Danjuan, Li, Meiyan, Zhou, Xingyao, Fu, Bo, Liu, Catherine C., Welsh, A. H.
The ultra-widefield (UWF) fundus image is an attractive 3D biomarker in AI-aided myopia screening because it provides much richer myopia-related information. Though axial length (AL) has been acknowledged to be highly related to the two key targets o
Externí odkaz:
http://arxiv.org/abs/2311.03967
We propose an original and general NOn-SEgmental (NOSE) approach for the detection of multiple change-points. NOSE identifies change-points by the non-negligibility of posterior estimates of the jump heights. Alternatively, under the Bayesian paradig
Externí odkaz:
http://arxiv.org/abs/2209.14995
We propose a new matrix factor model, named RaDFaM, which is strictly derived based on the general rank decomposition and assumes a structure of a high-dimensional vector factor model for each basis vector. RaDFaM contributes a novel class of low-ran
Externí odkaz:
http://arxiv.org/abs/2209.14846
It is an important task in the literature to check whether a fitted autoregressive moving average (ARMA) model is adequate, while the currently used tests may suffer from the size distortion problem when the underlying autoregressive models have low
Externí odkaz:
http://arxiv.org/abs/2209.09704
High-dimensional, higher-order tensor data are gaining prominence in a variety of fields, including but not limited to computer vision and network analysis. Tensor factor models, induced from noisy versions of tensor decompositions or factorizations,
Externí odkaz:
http://arxiv.org/abs/2206.02508
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Brecht, Ryan M., Liu, Catherine C., Beilinson, Helen A., Khitun, Alexandra, Slavoff, Sarah A., Schatz, David G.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Feb . 117(8), 4300-4309.
Externí odkaz:
https://www.jstor.org/stable/26929085
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Scandinavian Journal of Statistics; Mar2023, Vol. 50 Issue 1, p266-295, 30p