Performance of the regression models
Predicting student performance in higher education using multi-regression models
... This combination may lead to the improvement of the overall result. With large volumes of student data, including enrollment, academic and disciplinary records, higher education institution could build big data and ...
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Performance of likelihood-based estimation methods for multilevel binary regression models.
... the performance study and discuss briefly the estimation ...binary regression model with random intercepts and slopes, possibly correlated, and fixed slopes for the level 2 and cross-level interaction ...
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A Preliminary Performance Study on Nonlinear Regression Models using the Jaya Optimisation Algorithm
... The performance results for each benchmark regression model through various approaches, including PSO and Jaya, and when using four performance metrics are listed in Table ...
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Performance of Multi-City Land Use Regression Models for Nitrogen Dioxide and Fine Particles
... c itation : Wang M, Beelen R, Bellander T, Birk M, Cesaroni G, Cirach M, Cyrys J, de Hoogh K, Declercq C, Dimakopoulou K, Eeftens M, Eriksen KT, Forastiere F, Galassi C, Grivas G, Heinrich J, Hoffmann B, Ineichen A, ...
7
Performance Evaluation of Various Regression Models and Features for Prediction of Ozone Concentration
... Abstract Air pollution caused by ozone is a problem which threaten human health. Therefore, prediction of O 3 concentration is important. In this work, O 3 concentration level for Adana, Turkey is predicted with support ...
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High-dimensional Structured Additive Regression Models: Bayesian Regularisation, Smoothing and Predictive Performance
... ble regression techniques such as nonlinear and spatial effects with high-dimensional covariate ...geoadditive regression model for data from the Munich rental guide and an additive probit model for the ...
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The evaluation of it specialists’ performance based on the grading system and it’s forecasting by means of regression models
... the performance of IT specialists by the grading system using regression analysis, where the predictors are the individual potentials of IT ...The models for forecasting the professional ...
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Regression and ANN models for estimating minimum value of machining performance
... and regression model ...best regression model, a convenient approach is to evaluate all possible regression models ...best regression model and the results were sum- marized in Tables 9 ...
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On Some Ridge Regression Estimators for Logistic Regression Models
... ridge regression (RR) estimators for estimating the ridge regression parameter k for logistic regression model when the explanatory variables are highly ...the performance of the proposed ...
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Addressing multicollinearity in regression models: a ridge regression application
... ridge regression as an alternative to the ordinary least square method of estimation when there is multi-linearity between explanatory ...the performance of the proposed method, by using the mean square ...
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Addressing multicollinearity in regression models: a ridge regression application
... ridge regression as an alternative to the ordinary least square method of estimation when there is multi-linearity between explanatory ...the performance of the proposed method, by using the mean square ...
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VARIABLE SELECTION IN REGRESSION MODELS
... predicting performance under competition from perf , IT & MANAGEMENT Included in the International Serial Directories 46 Gaussian processes are a natural way of specifying prior distributions over functions of one ...
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Dynamic Integration of Regression Models
... base models’ MAE ± standard deviation for each data-set, and column 3-6 gives the MAE for each ensemble ...the performance of the base model, otherwise the entry is left ...
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The consolidation of forecests with regression models
... WLS regression when searching for the optimal forecasts consolidation model as this technique can be used to address the problem of deciding what the oldest forecasts should be to include in the ...the ...
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Diagnostic in Poisson Regression Models
... Poisson regression model is one of the most frequently used statistical methods as a standard method of data analysis in many ...the performance of the diagnostic ...
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Inference for Approximating Regression Models
... squares regression at the population ...that regression defines, rather than the slopes of an assumed linear model, become the population quantities of interest, and they can be estimated ...population ...
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Learning Uncertainty Models from Weather Forecast Performance Databases Using Quantile Regression
... quantile regression models are investigated in the ...superior performance of the local linear and kernel quantile regression ...of performance. All regression models ...
9
Generalized Inference in Linear Regression Models
... the performance of generalized confidence inter- vals (GCIs) of linear regression coefficients and dispersion parameters and generalized tests (GTs) for comparing regression coefficients for small ...
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Flexible Regression Models for Functional Responses.
... function-on-function regression models for functional response and functional covariate observed on possible different ...These models are a generalization of the standard FLMs, and allow the ...
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Flexible Regression Models for Survival Data
... GOR models and prove that all the parameters including the parameter ρ are identifiable in regression set- ...GOR models can be problematic as reported in many research ...the performance is ...
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