non linear regression
Artificial Neural Network and Non-Linear Regression: A Comparative Study
... nonlinear regression and classification problems in ...the linear and non-linear regression ...the linear and non-linear regression models are ...models. ...
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Linear and Non-Linear Regression: Powerful and Very Important Forecasting Methods
... not linear and trying to fit a linear equation to a data set that is inherently non-linear will result in a ...because non-linear regression is, in general, much more ...
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EVALUATING TWO NON-LINEAR REGRESSION MODELS IN WINTER CEREALS LEAF AREA GROWTH
... Beta. Non-linear investigation of plant growth in mathematic method might contribute a technique for estimating economic information of plant growth ...
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Back propagation neural network and non-linear regression models for dengue outbreak prediction
... From the above discussion, it can be concluded that neural network and regression model are likely to be able to predict dengue outbreak prediction based on location, time and data variability. Therefore, these ...
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Obesity and Health-Care Costs in Switzerland: Dealing with Endogeneity in Non-Linear Regression Models
... the non-IV ...the non-IV regression specification of BMI on ...from non-IV regression are subject to considerable bias due to the endogenous determination of ...CF regression ...
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Comparative non-linear regression - case of infiltration rate increase from upstream in valley
... of regression models found for the three boreholes of a consi- dered cross section are statistically ...the regression parameters (Härdle and Mammen, 1993; Neumeyer and Dette, ...
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Medium Term Electric Load Forecasting Using Multivariable Linear and Non Linear Regression
... lar, the forecasted peak load was 1 632 MW compared to the actual peak load of 2 230 MW. This corresponds to an absolute error of about 27%. On the other hand, the estimated peak loads using the proposed techniques re- ...
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Development of Mathematical Models for Predicting Customers Satisfaction in the Banking System with a Queuing Model Using Regression Method
... In organizational performance improvement, [10] sought to establish queuing models that can help organisations to improve on their customers’ service within and outside their establishment. The study created new models ...
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Comparative Evaluation of Multiple Linear Regression and Support vector Machine aided Linear and Non
... -linear regression in QSAR models. Models obtained from SVM aided non-linear regression were found statistically fit and more predictive than models obtained from multiple linear ...
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DMGroup at EmoInt 2017: Emotion Intensity Using Ensemble Method
... both linear and non-linear regression models and the result of stage1 serves as the input of the ...two regression models including linear regression and ...
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Linear Regression With Random Projections
... • In terms of numerical complexity, this approach is more efficient than non-linear regression and kernel ridge regression. Indeed, once the random space has been generated, we simply solve a ...
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Optimal control of fermentation processes
... According to Volesky and Votruba 1992; these methods can be divided into: i linear and non-linear regreSSIOn: based on conventional methods of mathematical statistics, ii momentum analys[r] ...
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Forage Legume Seeds Submitted to in Vitro and in Situ Fermentation Techniques
... Regression Non-linear regression analysis allowed the achievement of the equations that describe the behavior of germination percentage (GP%) and hardness percent- age (%HP) of the seeds ...
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QSAR study and rustic ligand-based virtual screening in a search for aminooxadiazole derivatives as PIM1 inhibitors
... a property of a given system and a set of descriptors that encode chemical information. A Genetic Algorithm tool was used to carried out the Genetic algorithm analysis (GFA) to reduce the number of the variables of the ...
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Latent Structure Linear Regression
... of linear least squares methods for standard linear ...standard linear regression is ...the regression task and the associated numerical ...of linear regressions within this ...
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Fitting parametric random effects models in very large data sets with application to VHA national data
... With the current focus on personalized medicine, pa- tient/subject level inference is often of key interest in translational research. GLMMs are a very rich class of models that are traditionally used to make such ...
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Inferential Models for Linear Regression
... In this paper we have elaborated on the recently proposed inferential model framework, which produces prior-free probabilistic summaries of evidence for/against assertions of interest and, moreover, these summaries have ...
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Refining Coarse-Grained Spatial Data Using Auxiliary Spatial Data Sets with Various Granularities
... many regression-based methods have been proposed that use auxiliary spatial data sets to re- fine coarse-grained target data (Flaxman, Wang, and Smola 2015; Smith, Mashhadi, and Capra 2014; Wang et ...2015). ...
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A Machine Learning Approach to Forecast Bitcoin Prices
... The Bitcoin digital currency has evolved from a novel financial experiment to a major currency with exchanges all over the globe. The current market capitalization of Bitcoin is more than $125 billion. The blockchain ...
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Robust Regression Diagnostics of Influential Observations in Linear Regression Model
... Regression diagnostics becomes necessary in regression analysis in order to detect the presence of outliers and influential points. These measures either use the OLS residuals or some functions of the OLS ...
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