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International Conference on Artificial Intelligence and Computational Intelligence

作者： Zou Kaiqi Zhou Xiaoyue Liu Ying Dalian Univ Technol Sch Engineer & Informat Dalian Peoples R China Dalian Maritime Univ Traff Logist Coll Dalian Peoples R China

ISBN: (纸本)9780769538167

Econometrics is based on economic data while the data represented by fuzzy sets can not be dealt with classical time series methods. In this paper the author proposes a new kind of variable named fuzzy variable of econometric model based on fuzzy membership function. The gap between fuzzy mathematics and econometrics is connected by the concept of fuzzy variable. An example of multivariate linear regression based on fuzzy variable is given to show how fuzzy variable can be used in econometrics.

关键词： fuzzy variable multivariate linear regression model econometrics

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multivariate linear regression model for Host Based Intrusion Detection 1

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1st International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Gautam, Sunil Kumar Om, Hari Indian Sch Mines Dept Comp Sci & Engn Dhanbad 826004 Bihar India

ISBN: (纸本)9788132222026;9788132222019

Computer security is an important issue for an organization due to increasing cyber-attacks. There exist some intelligent techniques for designing intrusion detection systems which can protect the computer and network systems. In this paper, we discuss multivariate linear regression model (MLRM) to develop an anomaly detection system for outlier detection in hardware profiles. We perform experiments on performance logfiles taken from a personal computer. Simulation results show that our model discovers intrusion effectively and efficiently.

关键词： Intrusion detection system multivariate linear regression model Mean square error Host based intrusion detection system Network based intrusion detection system

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Predicting short-term traffic flow in urban based on multivariate linear regression model

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1417-1427页

作者： Li, Dahui Qiqihar Univ Sch Comp & Control Engn Qiqihar Heilongjiang Peoples R China

In order to overcome the problems of low accuracy and time-consuming of traditional prediction methods for short-term traffic flow in urban, a prediction methods for short-term traffic flow in urban based on multiple linear regression model is proposed. The corresponding data attributes of short-term traffic flow in urban are selected by traffic operation status, and used as the original data of traffic flow prediction. According to the selected attributes, spatial static attributes data and traffic flow dynamic attributes data are collected, and fault data are identified and repaired. A multiple linear regression model for prediction of short-term traffic flow in urban is constructed to realize the prediction of short-term traffic flow in urban. The experimental results show that, compared with other methods, the average prediction accuracy of the proposed method is as high as 98.48%, and the prediction time is always less than 0.7 s, which is shorter.

关键词： multivariate linear regression model short-term traffic flow in urban flow prediction spatial static attributes

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Reduced rank regression with matrix projections for high-dimensional multivariate linear regression model

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ELECTRONIC JOURNAL OF STATISTICS 2021年第2期15卷 4167-4191页

作者： Guo, Wenxing Balakrishnan, Narayanaswamy Bian, Mengjie McMaster Univ Dept Math & Stat Hamilton ON L8S 4K1 Canada

In this work, we incorporate matrix projections into the reduced rank regression method, and then develop reduced rank regression estimators based on random projection and orthogonal projection in high-dimensional multivariate linear regression model. We propose a consistent estimator of the rank of the coefficient matrix and achieve prediction performance bounds for the proposed estimators based on mean squared errors. Finally, some simulation studies and a real data analysis are carried out to demonstrate that the proposed methods possess good stability, prediction performance and rank consistency compared to some other existing methods.

关键词： Matrix projection reduced rank regression dimension reduction high-dimensional data multivariate linear regression model

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DETECTION OF OUTLIERS IN THE multivariate linear-regression model

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 1989年第6期18卷 2225-2232页

作者： NAIK, DN Department of Mathematics and Statistics Old Dominion University Norfolk VA 23529 United States

In this article we suggest multivariate kurtosis as a statistic for detection of outliers in a multivariate linear regression model. The statistic has some local optimality properties.

关键词： multivariate linear regression model Detection of outliers multivariate kurtosis Locally best invariant test

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Prediction and Analysis of Aero-Material Consumption Based on multivariate linear regression model 3

Prediction and Analysis of Aero-Material Consumption Based o...

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3rd IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Yang, Yanming Naval Aviat Univ Qingdao Campus Qingdao 266041 Peoples R China

ISBN: (纸本)9781538643013

multivariate linear regression model has been widely used in society, economy, technology and science research. In this paper, the mathematic model and calculation method of multivariate linear regression model are introduced. The model test and interval estimation method of multivariate linear regression model is discussed. Aiming at the prediction of aero-material consumption, a multivariate linear regression prediction model of aviation material consumption was constructed by collecting the data of three basic monitoring indicators of aircraft tire consumption from 2001 to 2016. Based on the analysis of the examples, the model was tested and optimized by goodness of fit test, F-test, t-test and residual analysis, finally an accurate and reliable multivariate linear regression model was given. The results show that the linear regression model is feasible and effective for the prediction of aero-material spare parts consumption. The research results provide a realistic and scientific method for forecasting aero-material consumption.

关键词： aero-material consumption multivariate linear regression model prediction model test residual analysis

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Pricing model of Higher Education Tuition Based on multivariate linear regression

Pricing Model of Higher Education Tuition Based on Multivari...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Rao, Congjun Liu, Huanbin Huanggang Normal Univ Coll Math & Informat Sci Huanggang 438000 Peoples R China

ISBN: (纸本)9780769538167

In this paper, the pricing problem of higher education tuition standard is studied. Based on the statistical analysis method and grey system theory, the pricing model of higher education tuition is established. First of all, the factors of affecting the tuition pricing are analyzed through the grey relational analysis method. The results show that national average appropriation, training fee and family income are three main influence factors on tuition standard. Then, a multivariate linear regression model is established according to these three main influencing factors, and the least-square method of the model parameters estimation is given. Finally, two evaluation indexes are given to illustrate the rationality and feasibility of this pricing model.

关键词： higher education tuition standard multivariate linear regression model grey relational analysis

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model of baseline clinicopathological features predicts non-resolution of drug-induced liver injury at 6 months

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HEPATOLOGY INTERNATIONAL 2025年 1-14页

作者： Bihari, Chhagan Sharma, Shvetank Giri, Apoorva Yadav, Raj Pal Baweja, Sukriti Rastogi, Archana Sarin, Shiv Kumar Inst Liver & Biliary Sci Dept Pathol D1 Vasant Kunj New Delhi 110070 India Inst Liver & Biliary Sci Dept Mol & Cellular Med D1 Vasant Kunj New Delhi India Krishna Mohan Med Coll & Res Ctr Mathura India Inst Liver & Biliary Sci Dept Hepatol D1 Vasant Kunj New Delhi India

Introduction Chronicity in drug-induced liver injury (DILI) is assessed at 12 months, leading to a large time gap from its initial presentation. In this study, we developed a model that could predict biochemical non-resolution in DILI (DILI-NR) patients at 6 months using baseline clinicopathological data. Patients and methods Cases of DILI with liver biopsies were enrolled between January 2016 and December 2021. BSEP, MDR3, and MRP2 were assessed immunohistochemically. DILI-NR was considered a biochemical non-resolution 6 months after the onset of DILI. A separate cohort of 126 patients was taken as a validation cohort. Results DILI-NR was noted in 59/407 patients (14.5%). DILI-NR patients had significantly higher body mass index, lower hemoglobin, more severe disease at the presentation, autoantibody positivity, higher IgG, association with co-morbidities, and were more aged. Pathologically, DILI-NR had increased ductular reaction, duct damage, duct loss, ductular bile plugs, and autoimmune hepatitis-like morphology along with lesser expression of canalicular transporters. On multivariate logistic regression (LR) analysis and XGBoost analysis, BMI, hemoglobin, presence of autoantibodies, disease severity at baseline, and lower expression of any one transporter were associated with DILI-NR (AUROC = 0.92). After calibrating the model on the test cohort, the LR model showed AUROC of 0.89 with an accuracy of 87.3% and precision of 91.5%, confirming the effectiveness of the model. Conclusion The model encompassing hemoglobin, BMI, presence of autoantibodies, disease severity, and reduced expression of canalicular proteins at baseline predicts the biochemical non-resolution of DILI at six months.

关键词： DILI Chronic DILI Biochemical non-resolution DILI BMI Autoantibody Canalicular transporters Liver pathology Ductular bile plugs Duct damage multivariate linear regression model

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multivariate linear regression models for predicting metal content and sources in leafy vegetables and human health risk assessment in metal mining areas of Southern Jharkhand, India (Jan, 10.1007/s11356-021-12494-9, 2021)

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第21期28卷 27261-27261页

作者： Giri, Soma Mahato, Mukesh Kumar Singh, Abhay Kumar Cent Inst Min & Fuel Res CSIR Nat Resources & Environm Management Grp Barwa Rd Dhanbad 826015 Bihar India

A Correction to this paper has been published:

关键词： Metals Leafy vegetables Health risk assessment multivariate linear regression model

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SOME VARIABLE SELECTION PROCEDURES IN multivariate linear-regression modelS

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 1994年第2期41卷 205-214页

作者： HUANG, DY LIU, KC FENG CHIA UNIV DEPT STATSEATWEN TAICHUNGTAIWAN NATL CENT UNIV CHUNGLITAIWAN

In this paper, we propose a two-stage variable selection procedure for multivariate linear regression models. We select appropriate models under a guaranteed probability by using the summation of noncentralities in the first stage. In the second stage, we exclude those models with large individual noncentrality, and then select the best model with the minimum Akaike's information criterion (AIC). Empirical study is provided to show how to achieve our goal in variable selection and to demonstrate the efficiency and usefulness of the procedure in practical applications. In addition, we have built a reasonable model to ''plain and predict the earnings and productivity in Taiwan area.

关键词： multivariate linear regression model REDUCED model VARIABLE SELECTION PROCEDURE NON-CENTRALITY PARAMETER AIC INFORMATION MALLOW CP STATISTIC multivariate CP STATISTIC

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