Regression modelling
DAG-informed regression modelling, agent-based modelling, and microsimulation modelling: A critical comparison of methods for causal inference
... DAG-informed regression modelling and microsimulation/agent-based modelling with regard to how time is incorporated into the analyses—in terms of both scale and ...
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Regression modelling using priors depending on Fisher information covariance kernels (I priors)
... For regression modelling with I-priors, it is assumed that the regression functions lie in some vector space of ...for regression functions—this will be covered in Chapter ...
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Spline regression modelling of PTO performance of tractor fuelled with different biodiesels
... spline regression modelling of PTO power data showed that each fuel blend had similar behaviour, ...spline regression models (Equations (1)-(4)) provided good fittings with high coefficients of ...
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A computational approach to compare regression modelling strategies in prediction research
... the modelling strategies from ...linear regression modelling, data were entirely simulated, using Cholesky decomposition [33], and in all cases simulated variables followed a ran- dom normal ...
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Bias in odds ratios by logistic regression modelling and sample size
... to unbiased estimate of the population parameter value. As an external validating measure we compared the esti- mated parametric curve with nonparametric estimation of the regression function and calculated its ...
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Deep learning and regression modelling of cloudless downward longwave radiation
... the regression models; nonetheless, the new regression model outper- formed NN on the test ...for modelling cloudless DLR owing to its pecu- liar low mean bias errors, which could be tied to the ...
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Regression modelling of mortality
... the regression models developed in this thesis is to cause of death data, with the inclusion of correlations between surfaces in order to accommodate competing ...
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Classification of ordered texture images using regression modelling and granulometric features
... Abstract—Structural information available from the granu- lometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which ...
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Adaptive sampling technique using regression modelling and fuzzy inference system for network traffic
... The traffic consisting of packets for different applications were captured and their parameters, i.e. delay, jitter and percentage packet loss ratio were determined using Eqs. (1)-(3). The simulation duration was 8000 ...
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Bayesian Skew Normal Seemingly Unrelated Regression Modelling of Gross Regional Domestic Product
... The assumption of the error normality in the regression model was often questioned especially in cases where there was an outlier, which causes the behavior of asymmetric data. To overcome this, without data ...
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Logistic Regression Modelling for Complex Survey Data with an Application for Bed Net Use in Mozambique
... Several studies in health sciences analyzed data coming from complex sampling de- sign using different type of software [4]-[7], however none of them presents the speci- fication and the estimation methods behind the ...
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APPLICATION OF REGRESSION MODELLING TECHNIQUES IN DESALINATION OF SEA WATER BY MEMBRANE DISTILLATION
... concentration.The regression coefficient for feed flow rate in the model equation is ...The regression coefficient for feed temperature in the model equation is ...increases.The regression ...
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REGRESSION MODELLING AND GA OPTIMIZATION OF WEDM PROCESS FOR METAL MATRIX COMPOSITE
... The peak current, pulse-on time, pulse-off time and wire feed rate has been selected as input control factors.. Cutting rate has been selected as responses.[r] ...
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A general framework for functional regression modelling
... in regression models for functional ...functional regression in terms of corresponding models for scalar data, allowing the adaptation of a large body of existing methods for these novel ...includes ...
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Testing for a structural change of gradient in regression modelling
... statistical significance of the nodes in the model is assessed using bootstrap techniques. The method is illustrated using output data across the EU but more generally applies when modelling changes of gradient in ...
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Regression Modelling for Precipitation Prediction Using Genetic Algorithms
... Some techniques have been implemented in precipitation prediction. Using statistical model, precipitation can be predicted. The first model is Generalized Space-Time Autoregressive–Seemingly Unrelated Regression ...
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The forgotten semantics of regression modelling in Geography
... of regression models and, therefore, the meaning of predicted y, are shown to vary due to (1) choices about data: the specification of x (which covariates to include), the support of x (measurement scales and ...
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On the correspondence from Bayesian log linear modelling to logistic regression modelling with g priors
... logistic regression breaks, but only with regard to the intercept of the logistic ...logistic regression parameters, because the log-linear intercept does not contribute to the for- mation of the logistic ...
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Regression Estimation Modelling Techniques on Static Solar Photovoltaic Module
... different regression modelling techniques using the sparse based regression estimation algorithm on the static solar photovoltaic module ...three regression models were used to predict on a ...
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Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees
... are seldom used and most of the used data-driven approaches are based on arti fi cial neural networks or its variants. To the best of authors ’ knowledge, there are not any studies that investigated the applicability of ...
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