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A DYNAMIC logistic-regression model FOR MULTIPLE SPELL FAILURE TIME DATA

BIOMETRICAL JOURNAL 1988年第4期30卷 471-481页

作者： HAMERLE, A UNIV CONSTANCE STAT ABT D-7750 CONSTANCE GERMANY

The paper deals with discrete-time regression models to analyze multistate-multiepisode failure time data. The covariate process may include fixed and external as well as internal time dependent covariates. The effects of the covariates may differ among different kinds of failures and among successive episodes. A dynamic form of the logistic regression model is investigated and maximum likelihood estimation of the regression coefficients is discussed. In the last section we give an application of the model to the analysis of survival time after breast cancer operation.

关键词： Multistate‐multiepisode failure time data Discrete time models Time dependent covariates logistic regression model Maximum likelihood estimation

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Modified restricted Liu estimator in logistic regression model

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COMPUTATIONAL STATISTICS 2016年第4期31卷 1557-1567页

作者： Wu, Jibo Chongqing Univ Arts & Sci Sch Math & Finances Chongqing 402160 Peoples R China Chongqing Univ Arts & Sci Key Lab Grp & Graph Theories & Applicat Chongqing 402160 Peoples R China

Airay et al. (Commun Stat Simul Comput, 2014) proposed a restricted Liu estimator in logistic regression model with linear restrictions. However, this estimator did not satisfy the linear restrictions. In this paper, we introduce a modified restricted Liu estimator in logistic regression model with linear restrictions. Our results show that the new estimator satisfies the linear restrictions. We also discuss the properties of the new estimator under the matrix mean squared error criterion. Finally, a Monte Carlo study and a numerical example are given to show the performances of the new estimator.

关键词： Restricted maximum likelihood estimator Restricted Liu estimator Modified restricted Liu estimator logistic regression model

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Study on influencing factors of professionalization of migrant workers in construction Industry based on logistic regression model 2

Study on influencing factors of professionalization of migra...

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2nd International Conference on Applied Mathematics, modelling, and Intelligent Computing (CAMMIC)

作者： He Xiaoyu Zhu Donghua Zhao Yanjing Liu Tingting Anhui Xinhua Univ Anhui Higher Educ Inst Key Lab Bldg Struct Hefei Peoples R China

ISBN: (纸本)9781510655201;9781510655195

The construction of migrant workers professionalize-tion is the inevitable trend of the current construction industry development, by collating and analyzing the related literature at home and abroad, the article puts forward the theory model of the development of the construction workers professional and 10 factors affecting the development of construction workers professional, and using the logistic regression model analysis that it is concluded that the six key factors: Age, education level, vocational skills training, labor contract signing, social security participation and full-time degree, finally puts forward targeted development countermeasures and suggestions to enhance the attraction of the construction industry, popularize the formal employment mode, improve the technical training system.

关键词： Professionalization of migrant workers in construc-tion industry logistic regression model Key influencing factors Countermeasures and Suggestions

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The Analysis of Performance of Total Amount Reduction of Pollutants Emission Based on logistic regression model

The Analysis of Performance of Total Amount Reduction of Pol...

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Biennial International Conference on Ecological Informatics and Ecosystem Conservation (ISEIS)

作者： Wang Chunmei Sun Dezhi Wang Zhen Lin Zhaolan Beijing Forestry Univ Coll Environm Sci & Engn Beijing 100083 Peoples R China

Environmental policy is one of the main tools of environmental management for the government, it is very important for performance evaluation. This study is quantitative analysis for the relationship between the various types of environmental policies and the performance of total COD in wastewater emission volumes reducing, to obtain the role of pollution reduction from the support of policy. Based on the results of logistic model analysis, 5 reduction measures significantly affected the performance in three types of emission reduction targets. Among them, shutting down and relocate the number of enterprises have the greatest contribution to the emission reduction performance, and it followed by the sewage treatment rate, the removing of COD by the new wastewater treatment facilities, the number of projects completed under deadline management, the proportion of secondary industry. The frame measures are very effective and eliminating trailed productivity is the highest efficiency measure in all pollution control measures. Sewage treatment measures in this area also played a key role in pollution reduction;the sewage charging policy plays a key role in economic management. The adjustment of economic structure is concerned with pollution reduction. The more proportion of secondary industry covers, the more difficulty emission reducing is. Therefore, the government should pay more attention on the adjustment of industrial structure and strengthen the sewage treatment capacity to improve the performance of total amount reduction of pollutants emission.

关键词： logistic regression model Total Amount Reduction of Pollutants Emission Analysis of performance

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An Empirical Research on Technological Innovation Capability of Enterprises Based on logistic regression model

An Empirical Research on Technological Innovation Capability...

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International Conference on Computing, Information and Control (ICCIC 2011)

作者： Li, Na Zheng, Chuiyong Hohai Univ Nanjing Sch Business Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783642239977

Technological innovation is the power and source of enterprise development. According to the fundamental principle of logistic regression model, the paper did a principle component analysis of the selected indicators system to get the value of the dependent variable in the logistic regression model. Then, empirical studies were conducted with relevant data, getting the logistic regression formula to divide the enterprises into non-innovative enterprises and innovation enterprises with significantly high accuracy rate, and the analysis of the factors affecting technological innovation capability was carried out. We conclude that logistic regression model is an effective method for enterprise technological innovation capability research.

关键词： technological innovation capability technological innovative enterprises logistic regression model

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Rolling Bearing Reliability Estimation Based on logistic regression model

Rolling Bearing Reliability Estimation Based on Logistic Reg...

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International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering

作者： Li, Hongkun Wang, Yinhu Dalian Univ Technol Sch Mech Engn Dalian Peoples R China

ISBN: (纸本)9781479910144

Rolling bearing (RB) has been broadly applied on mechanical systems. Its reliability is directly related to the performance of the whole mechanical system. RB reliability estimation technology is crucial for mechanical system. logistic regression model (LRM) is constructed for RB reliability estimation in this paper. Vibration data acquisition and feature extraction are carried on for. Based on feature extraction investigation, root mean square and wavelet entropy are used to construct characteristics vector. Therefore, bearing degradation state information is determined by vibration information. Reliability estimation model based on LRM is constructed to estimate bearing performance. By using LRM, it has good performance on reliability estimation. It can be concluded that LRM is beneficial for RB life prediction.

关键词： rolling bearing reliability estimation logistic regression model vibration feature extraction

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Evaluation of Landslide Susceptibility Based on logistic regression model 5

Evaluation of Landslide Susceptibility Based on Logistic Reg...

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5th International Conference on Environmental Science and Material Application (ESMA)

作者： Du, Zhen Zhang, Biao Hu, Hong Bao, Junji Li, Wenbin Sch Nanchang Univ Nanchang 360031 Jiangxi Peoples R China

A lot of methods can be used for landslide susceptibility evaluation, such as support vector machine model, artificial neural network, etc. These models have good modeling effect, but often have the problem of low modeling efficiency. Hence, this paper proposes a simple and effective model of landslide susceptibility evaluation - logistic regression model. The Ningdu county of Jiangxi province in China, with 297 recorded landslides, was used as study case. The 6 environmental factors including elevation, slope, profile curvature, distance to rivers, lithology and NDVI were extracted in this study. The analysis showed that the significance of Profile curvature was greater than 0.05, and there was a collinearity problem, so it was excluded. After the establishment of the factor evaluation system, the prediction rate curve is used to evaluate the accuracy of the model. The results show that the AUC value of the prediction rate curve of logistic regression model is 0.864, indicating that the evaluation accuracy of logistic regression model is high and the modeling is reasonable. In addition, landslides in the study area are mainly distributed along both sides of the rivers, and elevation and lithology play a major role in the occurrence of landslides.

关键词： landslide Evaluation of susceptibility logistic regression model frequency ratio

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Functional Failure Probability Analysis Method of Dispatching Automation Master Station System Based on logistic regression model 36

Functional Failure Probability Analysis Method of Dispatchin...

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36th Chinese Control and Decision Conference (CCDC)

作者： Qian, Jianguo Kong, Piaohong Kan, Min Shi, Junxia Shi, Jianhua Zhao, Jianyu State Grid Zhejiang Elect Power Co LTD Angzhou Peoples R China State Grid Zhejiang Elect Power Co LTD Hangzhou Peoples R China State Grid Zhejiang Elect Power Co LTD Huzhou Power Supply Co Huzhou Peoples R China Nanjing Nanrui Informat Commun Technol Co LTD Nanjing Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The dispatching automation master station system (DAMSS) is an important component of power grid, responsible for real-time monitoring and control of the power grid's status. Its stability and reliability directly affect the safe operation of the power grid. Once the DAMSS experiences functional failures, it can pose significant safety risks and lead to serious power grid accidents. To address this issue, this paper proposes a functional failure probability analysis method of DAMSS based on logistic regression model. This method thoroughly analyzes the risk factors that may cause functional failure and establishes a logistic regression model capable of analyzing the functional failure probability. The model can make full use of the data information retained during the operation of DAMSS, using the status of risk factors as evidence input to the logistic regression model, and obtain the functional failure probability. Finally, a case study is conducted on a specific DAMSS to validate the effectiveness of the proposed method.

关键词： the dispatching automation master station system logistic regression model functional failure probability

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modeling and analysis of driver's decision making based on logistic regression model

Modeling and analysis of driver's decision making based on l...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Taguchi, Shun Inagaki, Shinkichi Suzuki, Tatsuya Hayakawa, Soichiro Tsuchida, Nuio Nagoya Univ Grad Sch Engn Dept Mech Sci & Engn Nagoya Aichi 4648601 Japan Toyota Technol Inst Tempaku Ku Nagoya Aichi Japan

ISBN: (纸本)9784907764289

This paper presents the development of a mathematical model of the human decision making. The main contributions of this paper are introduction of the logistic regression model as the mathematical model of the decision, development of the real time prediction of the decision based on the model, and proposal of some useful quantified measures to evaluate the characteristics of the human behavior. The proposed modeling and analysis strategies are applied to the driving behavior, in particular, focusing on the turn-right task in the intersections. Furthermore, two quantified measures "decision entropy" and "decision aggressiveness" are defined based on the estimated parameters in the logistic regression model. The objective evaluation made by these measures agrees well with the subjective evaluation made by the questionnaires.

关键词： logistic regression model decision making turn-right task

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Applying Machine Learning Technology for Weather Forecasting: A Case Study of the logistic regression model 24

Applying Machine Learning Technology for Weather Forecasting...

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1st International Conference on Image Processing Machine Learning and Pattern Recognition

作者： Shen, Kaiwei Beijing Inst Petrochem Technol China Big Data Management & Applicat Beijing Peoples R China

ISBN: (纸本)9798400707032

This study is focused on improving the dependability and precision of weather forecasting by employing the capabilities of Artificial Intelligence. Specifically, this study utilizes logistic regression and Machine Learning techniques to forecast weather, demonstrating the potential in optimizing weather-related activities and disaster management strategies. The study relies on comprehensive weather data observed over several years, sourced from Kaggle, and handles missing data and outliers during its pre-processing stages. The primary machine learning tool applied is logistic regression, followed by a stepwise feature selection to identify influential features for accurate weather prediction. The workflow also involves data collection, pre-processing, model building, training, and testing, with provisions for handling both numeric and categorical features along with imputations. The accuracy, precision, and recall of the prediction module are tested using appropriate statistical tools. The logistic regression model, upon implementation, demonstrated considerable accuracy, with an ability to predict rainy days and non-rainy days efficiently. An analytical approach was used to examine the model's sensitivity towards the removal of each feature, thereby ascertaining the relative importance of each. Critical predictors like 'Rainfall', 'Pressure9am', and 'WindGustSpeed' exhibited significant effects on the probability of rain. Overall, the use of logistic regression and Machine Learning techniques notably improved rain prediction, offering potential for further advancements in the field of weather forecasting.

关键词： Artificial intelligence Data preprocessing logistic regression model Weather forecasting

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