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Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models
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作者 Mosammat Mustari Khanaum Md Saidul Borhan +2 位作者 Farzana Ferdoush Mohammed Ali Nause Russel Mustafa Murshed 《Open Journal of Air Pollution》 2023年第4期142-159,共18页
Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for rep... Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers. 展开更多
关键词 regression analysis Air Quality Index linear Discriminant analysis Quadratic Discriminant analysis Logistic regression K-Nearest Neighbors Machine Learning Big Data analysis
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Isolated Area Load Forecasting using Linear Regression Analysis: Practical Approach 被引量:18
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作者 M. A. Mahmud 《Energy and Power Engineering》 2011年第4期547-550,共4页
This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through l... This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through linear regression and based on the identification of factors on which electrical load growth depends. To determine the identification factors, areas are selected whose histories of load growth rate known and the load growth deciding factors are similar to those of the isolated area. The proposed analysis is applied to an isolated area of Bangladesh, called Swandip where a past history of electrical load demand is not available and also there is no possibility of connecting the area with the main land grid system. 展开更多
关键词 ISOLATED Area LOAD Forecasting linear regression analysis (LRA).
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Finite Element Analysis and Linear Regression of Maximum Temperature for Inner Wall of Chimney Foundation 被引量:2
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作者 ZHANG Yu-mei SONG Yu-pu +1 位作者 ZHAO Yun-fei ZHANG Ye-min 《Journal of China University of Mining and Technology》 EI 2005年第3期234-240,共7页
The uniform design method was adopted and the twenty-four groups of different geometric and physical pa-rameters were chosen. The finite element model was built. Comparisons between the simulation results and the test... The uniform design method was adopted and the twenty-four groups of different geometric and physical pa-rameters were chosen. The finite element model was built. Comparisons between the simulation results and the test re-sults prove that the simulation results are correct. The distribution of the temperature field of the chimney foundationwas analyzed. The multivariate linear regression of the hightest tomperature was performed on the inner wall of thechimney foundation by the numerical calculated results. The fitting property of the highest temperature with six influ-ence factors was obtained. A simple method for the calculation of the temperature field of the chimney foundation wasprovided. 展开更多
关键词 chimney foundation temperature field nonlinear finite element analysis linear regression
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Using Linear Regression Analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic 被引量:1
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作者 Rodney Alexander 《Journal of Information Security》 2020年第4期261-291,共31页
The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during th... The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables). 展开更多
关键词 Information Assurance Defense in Depth Information Technology Network Security CYBERSECURITY linear regression analysis PANDEMIC
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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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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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A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis
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作者 Anas Basalamah Mahedi Hasan +1 位作者 Shovan Bhowmik Shaikh Akib Shahriyar 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期1921-1938,共18页
The recognition of pathological voice is considered a difficult task for speech analysis.Moreover,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysph... The recognition of pathological voice is considered a difficult task for speech analysis.Moreover,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this paper.We have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia detection.Several ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected dataset.The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models.According to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component Analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia. 展开更多
关键词 Dimensionality reduction dysphonia detection linear discriminant analysis logistic regression speech feature extraction support vector machine
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Linear Regression and Gradient Descent Method for Electricity Output Power Prediction
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作者 Yuanliang Liao 《Journal of Computer and Communications》 2019年第12期31-36,共6页
Regulating the power output for a power plant as demand for electricity fluctuates throughout the day is important for both economic purpose and the safety of the generator. In this work, gradient descent method toget... Regulating the power output for a power plant as demand for electricity fluctuates throughout the day is important for both economic purpose and the safety of the generator. In this work, gradient descent method together with regularization is investigated to study the electricity output related to vacuum level and temperature in the turbine. Ninety percent of the data was used to train the regression parameters while the remaining ten percent was used for validation. Final results showed that 99% accuracy could be obtained with this method. This opens a new window for electricity output prediction for power plants. 展开更多
关键词 Machine Learning linear ALGEBRA linear regression GRADIENT DESCENT Error analysis
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Empirical Likelihood Diagnosis of Modal Linear Regression Models
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作者 Shuling Wang Lin Zheng Jiangtao Dai 《Journal of Applied Mathematics and Physics》 2014年第10期948-952,共5页
In this paper, we investigate the empirical likelihood diagnosis of modal linear regression models. The empirical likelihood ratio function based on modal regression estimation method for the regression coefficient is... In this paper, we investigate the empirical likelihood diagnosis of modal linear regression models. The empirical likelihood ratio function based on modal regression estimation method for the regression coefficient is introduced. First, the estimation equation based on empirical likelihood method is established. Then, some diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation study. 展开更多
关键词 MODAL linear regression Model Empirical LIKELIHOOD OUTLIERS Influence analysis
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Optimal Batching Plan of Deoxidation Alloying based on Principal Component Analysis and Linear Programming
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作者 Zinan Zhao Shijie Li Shuaikang Li 《Journal of Mechanical Engineering Research》 2020年第2期11-16,共6页
As the market competition of steel mills is severe,deoxidization alloying is an important link in the metallurgical process.To solve this problem,principal component regression analysis is adopted to reduce the dimens... As the market competition of steel mills is severe,deoxidization alloying is an important link in the metallurgical process.To solve this problem,principal component regression analysis is adopted to reduce the dimension of influencing factors,and a reasonable and reliable prediction model of element yield is established.Based on the constraint conditions such as target cost function constraint,yield constraint and non-negative constraint,linear programming is adopted to design the lowest cost batting scheme that meets the national standards and production requirements.The research results provide a reliable optimization model for the deoxidization and alloying process of steel mills,which is of positive significance for improving the market competitiveness of steel mills,reducing waste discharge and protecting the environment. 展开更多
关键词 Deoxidization alloying Principal component regression analysis linear programming Optimization of dosing scheme
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Knowledge Discovery in Learning Management System Using Piecewise Linear Regression
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作者 S. Mythili R. Pradeep Kumar P. Nagabhushan 《Circuits and Systems》 2016年第11期3862-3873,共13页
Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. On... Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database. 展开更多
关键词 HISTOGRAM Piecewise linear regression Knowledge Discovery Big Data Cluster analysis
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Robust Linear Regression Models:Use of a Stable Distribution for the Response Data
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作者 Jorge A.Achcar Angela Achcar Edson Zangiacomi Martinez 《Open Journal of Statistics》 2013年第6期409-416,共8页
In this paper, we study some robustness aspects of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual nor... In this paper, we study some robustness aspects of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual normality assumption. It is well known that, in general, there is no closed form for the probability density function of stable distributions. However, under a Bayesian approach, the use of a latent or auxiliary random variable gives some simplification to obtain any posterior distribution when related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to two examples: one is related to a standard linear regression model with an explanatory variable and the other is related to a simulated data set assuming a 23 factorial experiment. Posterior summaries of interest are obtained using MCMC (Markov Chain Monte Carlo) methods and the OpenBugs software. 展开更多
关键词 Stable Distribution Bayesian analysis linear regression Models MCMC Methods OpenBugs Software
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Regression analysis and its application to oil and gas exploration:A case study of hydrocarbon loss recovery and porosity prediction,China
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作者 Yang Li Xiaoguang Li +3 位作者 Mingyu Guo Chang Chen Pengbo Ni Zijian Huang 《Energy Geoscience》 EI 2024年第4期240-252,共13页
In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not... In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery. 展开更多
关键词 regression analysis Oil and gas exploration Multiple linear regression model Nonlinear regression model Hydrocarbon loss recovery Porosity prediction
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Statistical Analysis of Leaf Water Use Efficiency and Physiology Traits of Winter Wheat Under Drought Condition 被引量:8
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作者 WU Xiao-li BAO Wei-kai 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2012年第1期82-89,共8页
Five statistical methods including simple correlation, multiple linear regression, stepwise regression, principal components, and path analysis were used to explore the relationship between leaf water use efficiency ... Five statistical methods including simple correlation, multiple linear regression, stepwise regression, principal components, and path analysis were used to explore the relationship between leaf water use efficiency (WUE) and physiological traits (photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, etc.) of 29 wheat cultivars. The results showed that photosynthesis rate, stomatal conductance, and transpiration rate were the most important leaf WUE parameters under drought condition. Based on the results of statistical analyses, principal component analysis could be the most suitable method to ascertain the relationship between leaf WUE and relative physiological traits. It is reasonable to assume that high leaf WUE wheat could be obtained by selecting breeding materials with high photosynthesis rate, low transpiration rate, and stomatal conductance under dry area. 展开更多
关键词 leaf water use efficiency multiple linear regression path analysis principal components simple correlation stepwise regression wheat genotype
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CFD Analysis and Optimization of Automobile Radiator based on STAR-CCM+ 被引量:4
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作者 XU Xiao-wen SUN Hou-huan +1 位作者 HUA Guang-sheng YIN Zhen-hua 《International Journal of Plant Engineering and Management》 2017年第4期212-221,共10页
This research talks about the radiator cooling system of the automobile engine,the radiator’ s fluidstructure interaction dynamics based on computational fluid dynamics( CFD) STAR-CCM+ software. The linear regression... This research talks about the radiator cooling system of the automobile engine,the radiator’ s fluidstructure interaction dynamics based on computational fluid dynamics( CFD) STAR-CCM+ software. The linear regression model of coolant determined by MATLAB software was imported into the user-defined field function of the software,using the standard K-Epsilon turbulence model to analyze temperature,pressure and velocity changes of the coolant in the radiator channel. In order to improve the efficiency of the radiator,it is necessary to analyze the structure of two kinds of heat sinks,and get better heat transfer effect. 展开更多
关键词 computational fluid dynamics(CFD) linear regression analysis RADIATOR STAR-CCM+
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Rainfall Estimation using Image Processing and Regression Model on DWR Rainfall Product for Delhi-NCR Region 被引量:1
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作者 Kuldeep Srivastava Ashish Nigam 《Journal of Atmospheric Science Research》 2020年第1期9-15,共7页
Observed rainfall is a very essential parameter for the analysis of rainfall,day to day weather forecast and its validation.The observed rainfall data is only available from five observatories of IMD;while no rainfall... Observed rainfall is a very essential parameter for the analysis of rainfall,day to day weather forecast and its validation.The observed rainfall data is only available from five observatories of IMD;while no rainfall data is available at various important locations in and around Delhi-NCR.However,the 24-hour rainfall data observed by Doppler Weather Radar(DWR)for entire Delhi and surrounding region(up to 150 km)is readily available in a pictorial form.In this paper,efforts have been made to derive/estimate the rainfall at desired locations using DWR hydrological products.Firstly,the rainfall at desired locations has been estimated from the precipitation accumulation product(PAC)of the DWR using image processing in Python language.After this,a linear regression model using the least square method has been developed in R language.Estimated and observed rainfall data of year 2018(July,August and September)was used to train the model.After this,the model was tested on rainfall data of year 2019(July,August and September)and validated.With the use of linear regression model,the error in mean rainfall estimation reduced by 46.58% and the error in max rainfall estimation reduced by 84.53% for the year 2019.The error in mean rainfall estimation reduced by 81.36% and the error in max rainfall estimation reduced by 33.81%for the year 2018.Thus,the rainfall can be estimated with a fair degree of accuracy at desired locations within the range of the Doppler Weather Radar using the radar rainfall products and the developed linear regression model. 展开更多
关键词 Rainfall estimation Rainfall analysis Doppler Weather Radar Precipitation Accumulation Product Image processing linear regression model
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Mapping QTL for Categorical Traits with Multivariate Regression
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作者 田佺 杨润清 《Journal of Shanghai Jiaotong university(Science)》 EI 2005年第S1期97-102,共6页
Simple linear regression analysis has been used to map QTL for quantitative traits. Many traits of biological interest and/or economical importance in various species show binary phenotypic distributions (e.g., presen... Simple linear regression analysis has been used to map QTL for quantitative traits. Many traits of biological interest and/or economical importance in various species show binary phenotypic distributions (e.g., presence or absence). It has been shown that such a binary trait also can be analyzed with the simple linear regression, subject to virtually no loss in power compared to the generalized linear model analysis. Binary trait is a special case of a multiple categorical trait (e.g., low, medium or high). We propose a mechanism to decompose a multiple categorical trait into an array of correlated binary variables. The categorical trait turned multiple binary traits are analyzed with a multivariate linear regression method. Turning the problem of categorical trait mapping into that of multivariate mapping allows the exploration of pleiotropic effects of QTL for different categories. Efficiency of the method is verified through a series of simulation experiments. 展开更多
关键词 CATEGORICAL TRAIT MAPPING QTL MULTIVARIATE linear regression analysis
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Biomass estimation of Shorea robusta with principal component analysis of satellite data
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作者 Nilanchal Patel Arnab Majumdar 《Journal of Forestry Research》 SCIE CAS CSCD 2010年第4期469-474,524,共7页
Spatio-temporal assessment of the above ground biomass (AGB) is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of tre... Spatio-temporal assessment of the above ground biomass (AGB) is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predomi- nantly covered by Sal (Shorea robusta C. F. Gaertn). Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components (PCs) of the bands, vegetation indices (VI), and the PCs of the VIs respectively and multiple linear regression (MLR) analysis be- tween the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs. 展开更多
关键词 above ground biomass spectral response modeling vegetation indices principal component analysis linear and multiple regression analysis.
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On the Covariance of Regression Coefficients
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作者 Pantelis G. Bagos Maria Adam 《Open Journal of Statistics》 2015年第7期680-701,共22页
In many applications, such as in multivariate meta-analysis or in the construction of multivariate models from summary statistics, the covariance of regression coefficients needs to be calculated without having access... In many applications, such as in multivariate meta-analysis or in the construction of multivariate models from summary statistics, the covariance of regression coefficients needs to be calculated without having access to individual patients’ data. In this work, we derive an alternative analytic expression for the covariance matrix of the regression coefficients in a multiple linear regression model. In contrast to the well-known expressions which make use of the cross-product matrix and hence require access to individual data, we express the covariance matrix of the regression coefficients directly in terms of covariance matrix of the explanatory variables. In particular, we show that the covariance matrix of the regression coefficients can be calculated using the matrix of the partial correlation coefficients of the explanatory variables, which in turn can be calculated easily from the correlation matrix of the explanatory variables. This is very important since the covariance matrix of the explanatory variables can be easily obtained or imputed using data from the literature, without requiring access to individual data. Two important applications of the method are discussed, namely the multivariate meta-analysis of regression coefficients and the so-called synthesis analysis, and the aim of which is to combine in a single predictive model, information from different variables. The estimator proposed in this work can increase the usefulness of these methods providing better results, as seen by application in a publicly available dataset. Source code is provided in the Appendix and in http://www.compgen.org/tools/regression. 展开更多
关键词 META-analysis linear regression COVARIANCE MATRIX regression COEFFICIENTS SYNTHESIS analysis
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A Recursive Binary Tree Model for the Analysis of the Response to Antiretroviral Therapy of HIV Infected Adults in Burkina Faso
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作者 Simon Tiendrébéogo Séni Kouanda +1 位作者 Blaise Somé Simplice Dossou-Gbeté 《Open Journal of Statistics》 2019年第6期643-656,共14页
In this paper we aim to analyse temporal variation of CD4 cell counts for HIV-infected individuals under antiretroviral therapy by using statistical methods. This is achieved by resorting to recursive binary regressio... In this paper we aim to analyse temporal variation of CD4 cell counts for HIV-infected individuals under antiretroviral therapy by using statistical methods. This is achieved by resorting to recursive binary regression tree approach [1]?[2]. This approach has made it possible to highlight the existence of several segments of the population of interest described by the interactions between the predictive covariates of the response to the treatment regimen. 展开更多
关键词 Model-Based CONDITIONAL regression Tree CD4 Cell COUNT Prediction linear Mixed Model Stability analysis ANTIRETROVIRAL Therapy
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