Zobrazeno 1 - 10
of 280
pro vyhledávání: '"Zou, Guohua"'
Autor:
Zhu, Hengkun, Zou, Guohua
Model averaging has received much attention in the past two decades, which integrates available information by averaging over potential models. Although various model averaging methods have been developed, there are few literatures on the theoretical
Externí odkaz:
http://arxiv.org/abs/2311.13827
This paper offers a new approach to address the model uncertainty in (potentially) divergent-dimensional single-index models (SIMs). We propose a model-averaging estimator based on cross-validation, which allows the dimension of covariates and the nu
Externí odkaz:
http://arxiv.org/abs/2112.15100
Support vector machine (SVM) is a well-known statistical technique for classification problems in machine learning and other fields. An important question for SVM is the selection of covariates (or features) for the model. Many studies have considere
Externí odkaz:
http://arxiv.org/abs/2112.14578
Autor:
Zhu, Hengkun, Zou, Guohua
Publikováno v:
In Journal of Statistical Planning and Inference July 2024 231
The fish target detection algorithm lacks a good quality data set, and the algorithm achieves real-time detection with lower power consumption on embedded devices, and it is difficult to balance the calculation speed and identification ability. To th
Externí odkaz:
http://arxiv.org/abs/2104.05050
Autor:
Wang, Miaomiao, Zou, Guohua
Model averaging is an alternative to model selection for dealing with model uncertainty, which is widely used and very valuable. However, most of the existing model averaging methods are proposed based on the least squares loss function, which could
Externí odkaz:
http://arxiv.org/abs/1910.12210
Autor:
Wang, Miaomiao, Zou, Guohua
Model averaging considers the model uncertainty and is an alternative to model selection. In this paper, we propose a frequentist model averaging estimator for composite quantile regressions. In recent years, research on these topics has been added a
Externí odkaz:
http://arxiv.org/abs/1910.12209
Smoothed AIC (S-AIC) and Smoothed BIC (S-BIC) are very widely used in model averaging and are very easily to implement. Especially, the optimal model averaging method MMA and JMA have only been well developed in linear models. Only by modifying, they
Externí odkaz:
http://arxiv.org/abs/1910.12208
The Horvitz-Thompson (HT) estimator is widely used in survey sampling. However, the variance of the HT estimator becomes large when the inclusion probabilities are highly heterogeneous. To overcome this shortcoming, in this paper, a hard-threshold me
Externí odkaz:
http://arxiv.org/abs/1804.04255
Publikováno v:
In Journal of Statistical Planning and Inference December 2022 221:100-113