Multivariable linear regression models (random effects) for the expression of each target for all four time points combined.J. Burton, MatthewN. Rajak, SaulH. Hu, VictorRamadhani, AthumaniHabtamu, EsmaelMassae, PatrickTadesse, ZerihunCallahan, Kelly...
Multivariable logistic and linear regression models for identification of clinically useful biomarkers for osteoarthritisOSTEOARTHRITISREGRESSION analysisBIOMARKERSJOINT painRADIOGRAPHYOsteoarthritis (OA) is the most common chronic degenerative joint disease which causes substantial joint pain, deformity and loss of ...
Using multivariable linear regression technique for modeling pro- ductivity construction in Iraq. OJCE. 2013; 3(3):127-35. CrossrefAl-Zwainy, F M S, Abdulmajeed, M H & Aljumaily, H S M 2013. Using multivariable linear regression technique for modelling productivity construction in Iraq. ...
multivariable regressiongeneralized linear modelsconjecturebuilding successful modelsSummary This chapter is devoted to model building and to the assumptions and limitations of standard regression methods and data mining techniques.doi:10.1002/0471463760.ch10Phillip I. Good...
be more accurat e t han t he models ident ified using regression. The st at e-space models ident ified using t he CVA algor it hm ar e especially accur at e. Introducti on A key r equir ement for many advanced cont r ol and monit or ing t echniques is t he availabilit y ...
The technique is based on implementing multivariable regression on previous year's hourly loads. Three regression models are investigated in this research: the linear, the polynomial, and the exponential power. The proposed models are applied to real loads of the Jordanian power system. Results ...
Poisson Regression, Logistic Regression, and Loglinear Models for Random Counts This chapter provides a unified discussion of Poisson regression, logistic regression, and loglinear modeling of contingency tables. These are three special cases of the general loglinear model, wherein expected category coun...
multivariable regression modelsprognostic estimatespiecewise linear regressionadditivity assumptiondistributional assumptionSchoenfeld residuallinearity assumptioncalibration and discriminationshrinkage factorsSummary This chapter contains sections titled: Introduction Preliminary Steps Data Reduction Verifying Model Assumptions: ...
Prediction of coal grindability based on petrography, proximate and ultimate analysis using multiple regression and artificial neural network models macrinite, resinite, and Rmax input sets with HGI in linear condition can achieve the correlation coefficients (R2) of 0.77, 0.75, and 0.81, respectively...
From direct observations, facial, vocal, gestural, physiological and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression and artificial neural network, have been proposed in the past decade. In ...