Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Dong, Yuexiao"'
Autor:
Li, Zeda, Dong, Yuexiao
This article introduces a novel and computationally fast model to study the association between covariates and power spectra of replicated time series. A random covariate-dependent Cram\'{e}r spectral representation and a semiparametric log-spectral
Externí odkaz:
http://arxiv.org/abs/2407.01763
Autor:
Soale, Abdul-Nasah, Dong, Yuexiao
Data visualization and dimension reduction for regression between a general metric space-valued response and Euclidean predictors is proposed. Current Fr\'ech\'et dimension reduction methods require that the response metric space be continuously embe
Externí odkaz:
http://arxiv.org/abs/2310.12402
We review sufficient dimension reduction (SDR) estimators with multivariate response in this paper. A wide range of SDR methods are characterized as inverse regression SDR estimators or forward regression SDR estimators. The inverse regression family
Externí odkaz:
http://arxiv.org/abs/2202.00876
Autor:
Soale, Abdul-Nasah, Dong, Yuexiao
In this paper, we introduce principal asymmetric least squares (PALS) as a unified framework for linear and nonlinear sufficient dimension reduction. Classical methods such as sliced inverse regression (Li, 1991) and principal support vector machines
Externí odkaz:
http://arxiv.org/abs/2002.05264
Autor:
Shen, Cencheng, Dong, Yuexiao
This paper introduces and investigates the utilization of maximum and average distance correlations for multivariate independence testing. We characterize their consistency properties in high-dimensional settings with respect to the number of margina
Externí odkaz:
http://arxiv.org/abs/2001.01095
Publikováno v:
In Journal of Statistical Planning and Inference September 2023 226:63-70
Autor:
Soale, Abdul-Nasah, Dong, Yuexiao
Moment-based sufficient dimension reduction methods such as sliced inverse regression may not work well in the presence of heteroscedasticity. We propose to first estimate the expectiles through kernel expectile regression, and then carry out dimensi
Externí odkaz:
http://arxiv.org/abs/1910.10898
The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC unde
Externí odkaz:
http://arxiv.org/abs/1906.01601
In statistical learning framework with regressions, interactions are the contributions to the response variable from the products of the explanatory variables. In high-dimensional problems, detecting interactions is challenging due to combinatorial c
Externí odkaz:
http://arxiv.org/abs/1901.07970
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.