scholarly journals The Impact on Life Satisfaction of Nursing Students Using the Fuzzy Regression Model

Author(s):  
Hee Sang Yoon ◽  
Seung Hoe Choi
2021 ◽  
pp. 1-11
Author(s):  
Yuan Tian

In recent years, social network analysis is one of the top 20 fields of artificial intelligence. Based on the network signal theory, this study examines the influence of the dual network embeddedness on the price premiums of Chinese initial public offerings (IPOs). In this paper we proposed fuzzy regression model for forecasting the impact of venture capital network and underwriter network on IPO premium based on some hypothesis. We find that: (1) Enterprises embedded in the central position of venture capital network will increase the IPO secondary market premium; (2) Secondly, employing underwriter in the central position of underwriting network will increase the IPO secondary market premium; (3) As venture capital are getting closer to the central position of venture capital network, the influence of underwriter network centrality in underwriting network on the increase of IPO secondary market reaction will gradually weaken. The research shows that occupying central position both in venture capital syndication network and underwriting network have the functions of sending signals, then increase the IPO secondary market premium, but the functions of different network signals will replace each other.


2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Pingping Gao ◽  
Yabin Gao

This paper presents a fuzzy regression analysis method based on a general quadrilateral interval type-2 fuzzy numbers, regarding the data outlier detection. The Euclidean distance for the general quadrilateral interval type-2 fuzzy numbers is provided. In the sense of Euclidean distance, some parameter estimation laws of the type-2 fuzzy linear regression model are designed. Then, the data outlier detection-oriented parameter estimation method is proposed using the data deletion-based type-2 fuzzy regression model. Moreover, based on the fuzzy regression model, by using the root mean squared error method, an impact evaluation rule is designed for detecting data outlier. An example is finally provided to validate the presented methods.


2017 ◽  
Vol 37 (2) ◽  
pp. 281-289 ◽  
Author(s):  
Narges Shafaei Bajestani ◽  
Ali Vahidian Kamyad ◽  
Ensieh Nasli Esfahani ◽  
Assef Zare

Sign in / Sign up

Export Citation Format

Share Document