regression-based prediction models
1 Models of forecasting in financial analysis of
... Bankruptcy prediction models based on the logistic regression for companies in 223. the Czech Republic[r] ...
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Prediction of Electricity Generation in Nigeria using Exponential Regression and Cobb-Douglas Models
... the prediction of electricity generation in Nigeria based on the available data for the year 2002 to ...Exponential regression and Cobb-Douglas models were applied to model electricity ...
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Multivariate adaptive regression splines models for vehicular emission prediction
... non-parametric models are adaptive and can exhibit a high degree of flexibility that may ultimately result in over fitting, if no measures are taken to coun- teract ...is based on a generalized ...
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Modeling for Prediction of Characteristic Deflection of Flexible Pavements- Comparison of Models Based on Artificial Neural Network and Multivariate Regression Analysis
... the regression model and the result of sensitivity analysis for the ANN model clearly indicate that the thickness of Bituminous Macadam has the greatest influence on the characteristic ...
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A global empirical system for probabilistic seasonal climate prediction
... climate prediction has been developed and validated. Multiple linear regression was chosen as the basis of the system; a simple predictor selec- tion scheme sought to maximise the predictive skill of a num- ...
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Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data
... the models, that is, how accu- rately the models predicted over the entire range of sever- ity, was compared using Hosmer-Lemeshow (HL) ...is based on comparing the observed and estimated mortality ...
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A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients
... learning models have been developed and used to predict virological response to ...such models to guide therapeutic decision making may be limited by two major ...these models heavily rely on ...
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Soil Quality Assessment By Near Infrared Spectroscopy: Predicting Ph And Soil Organic Carbon
... construct prediction models are 6 and all data generated good results with the number of LVs is less than ...of prediction models in NIRS ...PCR regression approach. Both methods work ...
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Prediction models for clustered data: comparison of a random intercept and standard regression model
... variance estimated with a random intercept logistic re- gression model [6]. Based on the six predictors and the latent random effect, the linear predictor lp was calculated for each patient. The linear predictor ...
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Sequential models for time-evolving regression problems with an application to energy demand prediction
... Here we adopt a statistical approach with a Bayesian perspective giving careful atten- tion to the general approach, but also showing useful specific examples. Key theoretical results, using the general linear dynamic ...
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Prediction of Soil Salinity Using Remote Sensing Tools and Linear Regression Model
... and regression analysis to predict and map soil salinity from data collected by the Advanced Spaceborne Thermal Emis- sion and Reflection Radiometer (ASTER) at the north of the Aq-Qala Region in northern ...
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REGRESSION MODELS
... a regression model, the personnel officer would first have to give the test to a sample of applicants and hire all of ...a prediction model to predict the widget production of future ...be based on ...
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Comparison of Artificial Neural Networks and Cox Regression Models in Prediction of Kidney Transplant Survival
... Considering an assumption distribution such as normal distribution for response variables, linearity of the suggested relationship, and variance of errors similarity are among the limitations of classical methods. ...
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An ANFIS-based Approach for Predicting the Manning Roughness Coefficient in Alluvial Channels at the Bank-full Stage
... method based on adaptive neuro-fuzzy inference system (ANFIS) for identifying Manning’s roughness coefficient, n , in modeling of alluvial channels ...sound prediction of hydraulic ...The regression ...
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Development of Prediction Models for the Mutagenicity of Nitrated PAHs Based on Multiple Linear Regression
... package. Based on the optimized geometries, 14 quantum-chemical structural descriptors were obtained, including energy of the highest occupied molecular orbital (E HOMO ), energy of the lowest unoccupied ...
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Validation of prediction models based on lasso regression with multiply imputed data
... Among the predictors and the outcome variable, data were missing in the range of 0 to 19%. All missing data were multiply imputed via the Multivariate Imputation by Chained Equations (MICE) procedure [21]. The imputa- ...
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Data Driven Analysis and Prediction using Regression Models on Iot based Drainage Monitoring System
... In most developing countries the drainage system has posed several problems to the environment and posing a threat to human life especially to the municipality workers. In the light of this issue this paper has ...
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A Meta-Stacked Software Bug Prognosticator Classifier
... Error prediction in open source software is more crucial due to its inherent complexity and the large repository of ...meta-stacked regression model (MSRM) which improvises the Rayleigh Probabilistic ...
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5633.pdf
... closures based on samples collected the previous ...addition, prediction models can be used to provide real-time estimates of ...statistical prediction methods like artificial neural networks ...
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Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data
... TCO prediction, both approaches were evaluated using each prediction variable ...TCO prediction by means of the SVR, considering all the variables, is accurate, with a mean absolute error (MAE) of ...
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