The Effects of Major Stakeholders on SMME's Performance Turnaround -- Empirical Analysis Based on the Ordinal Logistic Regression Model

2013 ◽  
Author(s):  
Li Qi
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Empirical Analysis ◽  
Logistic Regression Model ◽  
Ordinal Logistic Regression ◽  
Ordinal Logistic Regression Model

Quality Classification Model of Material Products Based on Ordinal Logistic Regression

2012 ◽  
Vol 460 ◽  
pp. 393-397 ◽  
Author(s):  
Peng Fei Mu ◽  
Dong Ling Zhang ◽  
Xiao Mei Xu ◽  
Yang Liu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Quality Evaluation ◽  
Ordinal Logistic Regression ◽  
Processing Method ◽  
Classification Model ◽  
Classification Result ◽  
Quality Classification ◽  
Ordinal Logistic Regression Model

It presents a proposed method for the development of quality evaluation and classification for material products, and shows the application of the ordinal logistic regression model and its advantages. It involved several steps: applying the linguistic information processing method, building the ordinal logistic regression model, differentiating and analyzing the quality evaluation to reach the quality classification result

An Ordinal Logistic Regression Model for the Masters Golf Tournament

CHANCE ◽  
2018 ◽  
Vol 31 (3) ◽  
pp. 44-58
Author(s):  
Erik L. Heiny ◽  
Cody C. Frisby
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Ordinal Logistic Regression ◽  
Golf Tournament ◽  
Ordinal Logistic Regression Model

scholarly journals Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories

2020 ◽  
Vol 12 (8) ◽  
pp. 3076
Author(s):  
Ting Xu ◽  
Yanjun Hao ◽  
Shichao Cui ◽  
Xingqi Wu ◽  
Zhishun Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Bayesian Model ◽  
Logistic Regression Model ◽  
Gaussian Mixture ◽  
Ordinal Logistic Regression ◽  
Crash Risk ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Ordinal Logistic Regression Model

The focus of this paper is the crash risk assessment of off-ramps in Xi’an. The time-to-collision (TTC) is used for the measurement and cross-comparison of the crash risk of each location. Five sites from the urban expressway in Xi’an were selected to explore the TTC distribution. An unmanned aerial vehicle and a camera were used to collect traffic flow data for 20 min at each site. The parameters, including speed, deceleration rate, truck percentage, traffic volume, and vehicle trajectories, were extracted from video images. The TTCs were calculated for each vehicle. The Gaussian mixture model (GMM) was proposed to predict the TTC probability density functions (PDFs) and cumulative density functions (CDFs) for five sites. The Kolmogorov–Smirnov (K-S) test indicated that the samples followed the estimated GMM distribution. The relationship between the crash risk level and influencing factors was studied by an ordinal logistic regression model and a naive Bayesian model. The results showed that the naive Bayesian model had an accuracy of 86.71%, while the ordinal logistic regression model had an accuracy of 84.81%. The naive Bayesian model outperformed the ordinal logistic regression model, and it could be applied to the real-time collision warning system.

Internal Control Quality Evaluation Model for Information System Based on QFD and Risk Analysis

2014 ◽  
Vol 643 ◽  
pp. 380-384
Author(s):  
Dong Ling Zhang ◽  
Xiu Zhi Zhu ◽  
Jie Qun Hua
Keyword(s):  
Information System ◽  
Logistic Regression ◽  
Risk Analysis ◽  
Regression Model ◽  
Internal Control ◽  
Logistic Regression Model ◽  
Quality Evaluation ◽  
Ordinal Logistic Regression ◽  
Control Quality ◽  
Ordinal Logistic Regression Model

Internal control Quality evaluation of the information system based on the risk management is a more significant issue in the corporate governance. It presents a proposed method for the development of quality assessment and risk evaluation and early-warning for internal control of the information system, and shows the application of the ordinal logistic regression model and its advantages. It involved several steps: processing a quality design through QFD based on the customer need, building a quality evaluation index system of internal control based on risk analysis, building the ordinal logistic regression model, differentiating and analyzing the quality evaluation to reach the internal control quality evaluation result.

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