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Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems
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作者 Zinovi Krougly Vladimir Krougly Serge Bays 《Applied Mathematics》 2023年第5期386-410,共25页
Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple... Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant. 展开更多
关键词 Matrix Algebra in multiple linear regression Numerical Integration High Precision Computation Applications in Predictive Emission Monitoring Systems
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Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network(ANN) and multiple linear regressions(MLR) 被引量:8
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作者 Ali Mohammadi Torkashvand Abbas Ahmadi Niloofar Layegh Nikravesh 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2017年第7期1634-1644,共11页
Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence s... Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions (MLR) and artificial neural networks (ANNs) are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations (nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg)) alone (P1), com- bination of nutrients concentrations (P2), nutrient concentration ratios alone (P3), and combination of nutrient concentrations and nutrient concentration ratios (P4). The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets (P1, P2 and P4). However, the application of P3 (N/Ca ratio) as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error (RMSE) were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration. 展开更多
关键词 artificial neural network FIRMNESS FRUIT KIWI multiple linear regression NUTRIENT
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Combined model based on optimized multi-variable grey model and multiple linear regression 被引量:11
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作者 Pingping Xiong Yaoguo Dang +1 位作者 Xianghua wu Xuemei Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第4期615-620,共6页
The construction method of background value is improved in the original multi-variable grey model (MGM(1,m)) from its source of construction errors. The MGM(1,m) with optimized background value is used to elimin... The construction method of background value is improved in the original multi-variable grey model (MGM(1,m)) from its source of construction errors. The MGM(1,m) with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM(1,2) with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction. 展开更多
关键词 multi-variable grey model (MGM(1 m)) backgroundvalue OPTIMIZATION multiple linear regression combined predic-tion model.
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A study of the mixed layer of the South China Sea based on the multiple linear regression 被引量:6
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作者 DUAN Rui YANG Kunde +1 位作者 MA Yuanliang HU Tao 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2012年第6期19-31,共13页
Multiple linear regression (MLR) method was applied to quantify the effects of the net heat flux (NHF), the net freshwater flux (NFF) and the wind stress on the mixed layer depth (MLD) of the South China Sea ... Multiple linear regression (MLR) method was applied to quantify the effects of the net heat flux (NHF), the net freshwater flux (NFF) and the wind stress on the mixed layer depth (MLD) of the South China Sea (SCS) based on the simple ocean data assimilation (SODA) dataset. The spatio-temporal distributions of the MLD, the buoyancy flux (combining the NHF and the NFF) and the wind stress of the SCS were presented. Then using an oceanic vertical mixing model, the MLD after a certain time under the same initial conditions but various pairs of boundary conditions (the three factors) was simulated. Applying the MLR method to the results, regression equations which modeling the relationship between the simulated MLD and the three factors were calculated. The equations indicate that when the NHF was negative, it was the primary driver of the mixed layer deepening; and when the NHF was positive, the wind stress played a more important role than that of the NHF while the NFF had the least effect. When the NHF was positive, the relative quantitative effects of the wind stress, the NHF, and the NFF were about i0, 6 and 2. The above conclusions were applied to explaining the spatio-temporal distributions of the MLD in the SCS and thus proved to be valid. 展开更多
关键词 mixed layer multiple linear regression South China Sea vertical mixing model
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Application of Multiple Linear Regression and Manova to Evaluate Health Impacts Due to Changing River Water Quality 被引量:2
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作者 Sudevi Basu K. S. Lokesh 《Applied Mathematics》 2014年第5期799-807,共9页
Rivers are important systems which provide water to fulfill human needs. However, excessive human uses over the years have led to deterioration in quality of river causing, causing health problems from contaminated wa... Rivers are important systems which provide water to fulfill human needs. However, excessive human uses over the years have led to deterioration in quality of river causing, causing health problems from contaminated water. This study focuses on the application of statistical techniques, Multiple Linear Regression model and MANOVA to assess health impacts due to pollution in Cauvery river stretch in Srirangapatna. In this study, using Multiple Linear Regression, it is found that health impact level is 60.8% dependent on water quality parameters of BOD, COD, TDS, TC and FC. The t-statistics and their associated 2-tailed p-values indicate that COD and TDS produces health impacts compared to BOD, TC and FC, when their effects are put together across all the six sampling stations in Srirangapatna. Further Pearson correlation Matrix shows highly significant positive correlation amongst parameters across all stations indicating possibility of common sources of origin that might be anthropogenic. Also graphs are plotted for individual parameters across all stations and it reveals that COD and TDS values are significant across all sampling stations, though their values are higher in impact stations, causing health impacts. 展开更多
关键词 multiple linear regression Model MANOVA t-Statistics BOD COD TDS TC FC
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Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and Random Forest 被引量:2
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作者 LIU Ying-xia Gerard B.M.HEUVELINK +4 位作者 Zhanguo BAI HE Ping JIANG Rong HUANG Shaohui XU Xin-peng 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2022年第12期3637-3657,共21页
Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the applica... Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability. 展开更多
关键词 partial factor productivity of N partial nutrient balance of N stepwise multiple linear regression Random Forest county scale Northeast China
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Hole Cleaning Prediction in Foam Drilling Using Artificial Neural Network and Multiple Linear Regression 被引量:3
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作者 Reza Rooki Faramarz Doulati Ardejani Ali Moradzadeh 《Geomaterials》 2014年第1期47-53,共7页
Foam drilling is increasingly used to develop low pressure reservoirs or highly depleted mature reservoirs because of minimizing the formation damage and potential hazardous drilling problems. Prediction of the cuttin... Foam drilling is increasingly used to develop low pressure reservoirs or highly depleted mature reservoirs because of minimizing the formation damage and potential hazardous drilling problems. Prediction of the cuttings concentration in the wellbore annulus as a function of operational drilling parameters such as wellbore geometry, pumping rate, drilling fluid rheology and density and maximum drilling rate is very important for optimizing these parameters. This paper describes a simple and more reliable artificial neural network (ANN) method and multiple linear regression (MLR) to predict cuttings concentration during foam drilling operation. This model is applicable for various borehole conditions using some critical parameters associated with foam velocity, foam quality, hole geometry, subsurface condition (pressure and temperature) and pipe rotation. The average absolute percent relative error (AAPE) between the experimental cuttings concentration and ANN model is less than 6%, and using MLR, AAPE is less than 9%. A comparison of the ANN and mechanistic model was done. The AAPE values for all datasets in this study were 3.2%, 8.5% and 10.3% for ANN model, MLR model and mechanistic model respectively. The results show high ability of ANN in prediction with respect to statistical methods. 展开更多
关键词 Foam DRILLING HOLE CLEANING Artificial NEURAL Network multiple linear regression
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Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming 被引量:1
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作者 Bijan Afrasiabian Mosleh Eftekhari 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2022年第5期1421-1432,共12页
Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to p... Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and elastic modulus(E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets.Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination(R2),root mean square error(RMSE), and mean absolute error(MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156,respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2value and lower errors. 展开更多
关键词 Mode I fracture Toughness Critical stress intensity factor linear multiple regression(LMR) Gene expression programming(GEP)
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Analysis of the Invariance and Generalizability of Multiple Linear Regression Model Results Obtained from Maslach Burnout Scale through Jackknife Method
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作者 Tolga Zaman Kamil Alakus 《Open Journal of Statistics》 2015年第7期645-651,共7页
The purpose of this study was to examine the burnout levels of research assistants in Ondokuz Mayis University and to examine the results of multiple linear regression model based on the results obtained from Maslach ... The purpose of this study was to examine the burnout levels of research assistants in Ondokuz Mayis University and to examine the results of multiple linear regression model based on the results obtained from Maslach Burnout Scale with Jackknife Method in terms of validity and generalizability. To do this, a questionnaire was given to 11 research assistants working at Ondokuz Mayis University and the burnout scores of this questionnaire were taken as the dependent variable of the multiple linear regression model. The variable of burnout was explained with the variables of age, weekly hours of classes taught, monthly average credit card debt, numbers of published articles and reports, gender, marital status, number of children and the departments of the research assistants. Dummy variables were assigned to the variables of gender, marital status, number of children and the departments of the research assistants and thus, they were made quantitative. The significance of the model as a result of multiple linear regressions was examined through backward elimination method. After this, for the five explanatory variables which influenced the variable of burnout, standardized model coefficients and coefficients of determination, and 95% confidence intervals of these values were estimated through Jackknife Method and the generalizability of the parameter estimation results of these variables on population was researched. 展开更多
关键词 JACKKNIFE METHOD INVARIANCE GENERALIZABILITY Maslach BURNOUT SCALE multiple linear regression Backward Elimination METHOD
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Predicting urbanization level by main element analysis and multiple linear regression---taking Xiantao district in Hubei Province as an example
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作者 Li BingyiDepartment of Urban Planning & Architecture, Wuhan Urban Construction Institute,Wuhan 430074, CHINA 《Journal of Geographical Sciences》 SCIE CSCD 1998年第1期90-91,93-94,共4页
In this paper we firstly select main factors relating to urbanization level of Xiantao District in Hubei Province by main element, then, make model of urbanization level by analysis of multiple liner regression, and l... In this paper we firstly select main factors relating to urbanization level of Xiantao District in Hubei Province by main element, then, make model of urbanization level by analysis of multiple liner regression, and lastly predict its urbanization level 展开更多
关键词 urbanization level main element analysis multiple linear regression Xiantao Hubei PROVINCE
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Prediction of Anti-Inflammatory Activity of a Series of Pyrimidine Derivatives, by Multiple Linear Regression and Artificial Neural Networks
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作者 Yafigui Traoré Jean Missa Ehouman +2 位作者 Mamadou Guy-Richard Koné Donourou Diabaté Nahossé Ziao 《Computational Chemistry》 CAS 2022年第4期186-202,共17页
Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descripto... Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μ<sub>D</sub>, the energy of the highest occupied molecular orbital E<sub>HOMO</sub>, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R<sup>2</sup>=91.28%, R<sup>2</sup><sub>aj</sub>=89.11%, RMCE = 0.2831, R<sup>2</sup><sub>ext</sub>=86.50% and R<sup>2</sup>=98.22%, R<sup>2</sup><sub>aj</sub>=97.75%, RMCE = 0.1131, R<sup>2</sup><sub>ext</sub>=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives. 展开更多
关键词 Anti-Inflammatory Activity multiple linear regression Artificial Neural Network QSAR
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Correlation Analysis of Fiscal Revenue and Housing Sales Price Based on Multiple Linear Regression Model
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作者 Wei Zheng Xinyi Li +1 位作者 Nanxing Guan Kun Zhang 《数学计算(中英文版)》 2020年第1期3-12,共10页
This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis a... This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points. 展开更多
关键词 Financial Revenue Housing Sales Price Correlation Analysis multiple linear regression Model
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Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas
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作者 Rokaya Mouhibi Mohamed Zahouily +1 位作者 Khalid El Akri Naima Hanafi 《Open Journal of Medicinal Chemistry》 2013年第1期7-15,共9页
Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR... Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R7), the partition coefficient between n-octanol and water logP and logP(R1) and Molecular weight MW(R7), showed good statistics both in the regression and artificial neural network with a configuration of (4-3-1) by using Bayesian and Leven-berg-Marquardt Methods. Comparison of the descriptor’s contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to activity may be non-linear. 展开更多
关键词 Artificial Neural Network DESCRIPTORS CCR5 multiple linear regression Structure-Activity Relationship
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Research on the Impact of Employment on GDP Based on Multiple Linear Regression Model
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作者 Wei Zheng Yao Xu +1 位作者 Jun Yang Shuhuan Yang 《经济管理学刊(中英文版)》 2022年第1期1-8,共8页
In order to study the impact of employed persons in various industries on regional GDP,based on the data of GDP in various regions and employed persons divided by industries in various regions in 2019,the employed per... In order to study the impact of employed persons in various industries on regional GDP,based on the data of GDP in various regions and employed persons divided by industries in various regions in 2019,the employed persons are divided into seven categories,and the multiple linear regression model of GDP in various regions of China on employed persons in various industries is established by using the methods of multiple linear regression analysis and cluster analysis,It also analyzes the impact of employees in various industries on the GDP of various regions. 展开更多
关键词 GDP Employees in Various Industries multiple linear regression
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Predicting the Acute Toxicity of Aromatic Amines by Linear and Nonlinear Regression Methods 被引量:4
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作者 张晓龙 周志祥 +3 位作者 刘阳华 范雪兰 李捍东 王建涛 《Chinese Journal of Structural Chemistry》 SCIE CAS CSCD 2014年第2期244-252,共9页
In current paper, a quantitative structure-activity relationship (QSAR) study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of ... In current paper, a quantitative structure-activity relationship (QSAR) study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression (MLR) and support vector machine (SVM) were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness. 展开更多
关键词 aromatic amines acute toxicity quantitative structure-activity relationship(QSAR) support vector machine (SVM) multiple linear regression (MLR)
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Prediction of Wind Speed Using a Hybrid Regression-Optimization Approach
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作者 Bhuvana Ramachandran Anbazhagan Swaminathan 《Journal of Power and Energy Engineering》 2023年第7期21-35,共15页
Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to pr... Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to predict wind speed, and a hybrid optimization approach is one of them. In this paper, the hybrid optimization approach combines a multiple linear regression approach with an optimization technique to achieve better results. In the context of wind speed prediction, this hybrid optimization approach can be used to improve the accuracy of existing prediction models. Here, a Grey Wolf Optimizer based Wind Speed Prediction (GWO-WSP) method is proposed. This approach is tested on the 2016, 2017, 2018, and 2019 Raw Data files from the Great Lakes Environmental Research Laboratories and the National Oceanic and Atmospheric Administration’s (GLERL-NOAA) Chicago Metadata Archive. The test results show that the implementation is successful and the approach yields accurate and feasible results. The computation time for execution of the algorithm is also superior compared to the existing methods in literature. 展开更多
关键词 Wind Speed Prediction multiple linear regression Grey Wolf Optimizer Accuracy of Results Wind Power
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The Linear Regression Method for Improving Magnetic Parameter of the Electroless CoFeB Films
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作者 LIU Changhui HE Huahui SHEN Xiang LI Haihua 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2008年第2期207-211,共5页
The kinetics equation of deposition rate was implemented to help explain some of the mechanisms responsible for structures observed during the deposition of CoFeB films on poly-ester plastic. The plating rate of elect... The kinetics equation of deposition rate was implemented to help explain some of the mechanisms responsible for structures observed during the deposition of CoFeB films on poly-ester plastic. The plating rate of electroless CoFeB films is a function of concentration of sodium tetrahydroborate, pH of the plating bath, plating temperature and the metallic ratio. The estimated regression coefficient, confidence interval, residual error and confidence interval were confirmed by computer program. The optimal composition of the plating bath was obtained and the dynamic electromagnetic parameters of films were measured in the 2-10 GHz range. At 2 GHz, the permeability, magnetic loss of the electroless CoFeB films were 304,76.6 respectively as the concentration of reducer is 1 g·L^-1. 展开更多
关键词 deposition rate CoFeB ternary alloy multiple linear regression PERMEABILITY
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Recognition Method for Change Point of Traffic Flow Linear Regressions
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作者 张敬磊 王晓原 马立云 《Journal of Donghua University(English Edition)》 EI CAS 2012年第1期59-61,共3页
Recognition method of traffic flow change point was put forward based on traffic flow theory and the statistical change point analysis of multiple linear regressions. The method was calibrated and tested with the fiel... Recognition method of traffic flow change point was put forward based on traffic flow theory and the statistical change point analysis of multiple linear regressions. The method was calibrated and tested with the field data of Liantong Road of Zibo city to verify the validity and the feasibility of the theory. The results show that change point method of multiple linear regression can make out the rule of quantitative changes in traffic flow more accurately than ordinary methods. So, the change point method can be applied to traffic information management system more effectively. 展开更多
关键词 traffic flow quantitative changes multiple linear regressions change point recognition
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Simple Linear Regression Model for Hidden/Hard-to-Reach/Elusive Populations
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作者 Maahi Tuahiru Mubarika Alhassan Haadi Abdul-Rahaman 《Open Journal of Statistics》 2017年第4期551-559,共9页
Estimation methods have over the years been a problem for Statistician especially in sectors that have to do with Hidden/Hard-to-Reach population. In this paper, a regression model was derived for Elusive/Hard-to-Reac... Estimation methods have over the years been a problem for Statistician especially in sectors that have to do with Hidden/Hard-to-Reach population. In this paper, a regression model was derived for Elusive/Hard-to-Reach/Hidden populations. This was achieved by modelling the Multiplicity Estimator given by Birnbaum and Sirken (1965) into a regression model. The paper also gave the least-squares estimation of the unknown parameters β0 and β1, and σ2. 展开更多
关键词 multiplICITY ESTIMATOR SIMPLE linear regression Least SQUARES Estimation
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A Universal Selection Method in Linear Regression Models
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作者 Eckhard Liebscher 《Open Journal of Statistics》 2012年第2期153-162,共10页
In this paper we consider a linear regression model with fixed design. A new rule for the selection of a relevant submodel is introduced on the basis of parameter tests. One particular feature of the rule is that subj... In this paper we consider a linear regression model with fixed design. A new rule for the selection of a relevant submodel is introduced on the basis of parameter tests. One particular feature of the rule is that subjective grading of the model complexity can be incorporated. We provide bounds for the mis-selection error. Simulations show that by using the proposed selection rule, the mis-selection error can be controlled uniformly. 展开更多
关键词 linear regression MODEL SELECTION multiple TESTS
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