Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Yunquan Song"'
Autor:
Yiming Hou, Yunquan Song
Publikováno v:
Axioms, Vol 13, Iss 8, p 517 (2024)
Transfer learning, as a machine learning approach enhancing model generalization across different domains, has extensive applications in various fields. However, the risk of privacy leakage remains a crucial consideration during the transfer learning
Externí odkaz:
https://doaj.org/article/57f70d7314bb43fd9090e6d57db0f69c
Publikováno v:
Axioms, Vol 13, Iss 5, p 315 (2024)
This study addresses the problem of parameter estimation in spatial autoregressive models with missing data and measurement errors in covariates. Specifically, a corrected likelihood estimation approach is employed to rectify the bias in the log-maxi
Externí odkaz:
https://doaj.org/article/db4b697011bb438693e48df2a26d84b1
Publikováno v:
Axioms, Vol 13, Iss 1, p 4 (2023)
With the widespread application of spatial data in fields like econometrics and geographic information science, the methods to enhance the robustness of spatial econometric model estimation and variable selection have become a central focus of resear
Externí odkaz:
https://doaj.org/article/0956ea67dc274e61a16e54014578b2d0
Publikováno v:
AIMS Mathematics, Vol 6, Iss 7, Pp 7125-7152 (2021)
In this paper, we consider the statistical inferences for varying coefficient partially nonlinear model with missing responses. Firstly, we employ the profile nonlinear least squares estimation based on the weighted imputation method to estimate the
Externí odkaz:
https://doaj.org/article/c342b6a2fdab4862962cffb27c257dee
Publikováno v:
Entropy, Vol 25, Iss 2, p 230 (2023)
As spatial correlation and heterogeneity often coincide in the data, we propose a spatial single-index varying-coefficient model. For the model, in this paper, a robust variable selection method based on spline estimation and exponential squared loss
Externí odkaz:
https://doaj.org/article/a32d78baf4fd46d989597778f445ae0d
Publikováno v:
Entropy, Vol 25, Iss 2, p 249 (2023)
With the continuous application of spatial dependent data in various fields, spatial econometric models have attracted more and more attention. In this paper, a robust variable selection method based on exponential squared loss and adaptive lasso is
Externí odkaz:
https://doaj.org/article/e42eb0f4045b448fbd0f876183ce7f71
Publikováno v:
Entropy, Vol 24, Iss 11, p 1660 (2022)
In recent years, spatial data widely exist in various fields such as finance, geology, environment, and natural science. These data collected by many scholars often have geographical characteristics. The spatial autoregressive model is a general meth
Externí odkaz:
https://doaj.org/article/cb984a04e15244e5b4bdca6d1fb328c5
Publikováno v:
Mathematics, Vol 10, Iss 16, p 2985 (2022)
Variable selection has been a hot topic, with various popular methods including lasso, SCAD, and elastic net. These penalized regression algorithms remain sensitive to noisy data. Furthermore, “concept drift” fundamentally distinguishes streaming
Externí odkaz:
https://doaj.org/article/96555565527945c6a26838952f8776c6
Publikováno v:
Mathematics, Vol 10, Iss 17, p 3095 (2022)
When the spatial response variables are discrete, the spatial logistic autoregressive model adds an additional network structure to the ordinary logistic regression model to improve the classification accuracy. With the emergence of high-dimensional
Externí odkaz:
https://doaj.org/article/7e02526950cb4cad8f89aacd5b3969c0
Publikováno v:
IEEE Access, Vol 7, Pp 66895-66909 (2019)
Oil slicks from ships or oil platforms cause serious damage to the marine and coastal environment and ecosystems. To monitor such spill events, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been widely used in improving oil spillage
Externí odkaz:
https://doaj.org/article/18d55309e5c044adb81f8e60c3440d6d