nonlinear regression model (NLRM)
The large deviation for the least squares estimator of nonlinear regression model based on WOD errors
... As a kind of dependent random variables, the widely orthant dependent random variables, or WOD for short, have a very important place in dependence structures for the intricate properties. And so its behavior and ...
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Multiple nonlinear regression model for predicting the optical performances of dielectric crossed compound parabolic concentrator (dCCPC)
... In this study, several mathematical models are proposed through multiple nonlinear regression based on a mass of simulation results, in order to predict the optical performance for basec[r] ...
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A comparative study for back propagation neural network and nonlinear regression models for predicting dengue outbreak
... This study aims to design a Neural Network Model (NNM) and Nonlinear Regression Model (NLRM) using different architectures and parameters incorporating time series,[r] ...
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Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices
... The model is obtained by a two-step iterative process that combines an overall model (least-squares fitting) with a local model (fuzzy k-nearest- neighbour) to take advantage of their complementary ...
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Local Curvature and Centering Effects in Nonlinear Regression Models
... or nonlinear model. The properties of the assumed model are thus relevant to the estimation and testing of parameters defined within the fitted local ...p-dimensional nonlinear ...
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CBPS Based Inference in Nonlinear Regression Models with Missing Data
... the nonlinear regression model with missing ...outcome regression model or the propensity score model is correctly specified, the proposed estimators perform as well as ...
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A Technique for Estimator Densities applied to Exponential Regression, Nonlinear Regression models and Biochemical Titration Curves
... statistical model, typically a nonlinear regression model ...estimation model either agrees with or differs from the model that has generated the ...ponential regression, ...
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Analysis of Flight Fuel Consumption Based on Nonlinear Regression
... A330 model is used as the sample to analyze the ...a model. In this study, the least squares method and the nonlinear regression model were used to analyze the influence of each ...
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Technological progress and its contribution to economic development
... the nonlinear regression model we know, that when in a given country TAI = 0,05, and the TAI value increases by 1 percentage point, it will probably cause increase in HDI value of approximately 4,99 ...
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PREDICTION OF GROUND VIBRATIONS IN OPENCAST MINE USING NONLINEAR REGRESSION ANALYSIS
... using Nonlinear regression ...linear regression analysis have been compared with actual values observed from the field and are very close with the field ...the Nonlinear regression ...
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A De Novo programming approach for a robust closed-loop supply chain network design under uncertainty: An M/M/1 queueing model Pages 211-228 Download PDF
... chain model, which considers the effects of establishing a queueing system in each recovery ...this model to cope with the uncertainty of the ...proposed nonlinear bi-objective model, the ...
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Comparison of fractional splines with polynomial splines; An Application on under-five year’s child mortality data in Pakistan (1960-2012)
... Spline regression models which are also called piecewise or segmented line regression models or broken stick regression models framed of continual linear ...spline regression models may sound ...
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Evaluation of moving bed biofilm reactor (MBBR) by applying adaptive neuro-fuzzy inference systeme (ANFIS), radial basis function (RBF) and Fuzzy Regression Analysis
... The purpose of this study is to investigate the accuracy of predictions of aniline removal efficiency in a moving bed biofilm reactor (MBBR) by various methods, namely by RBF, ANFIS, and fuzzy regression analysis. ...
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Estimation and inference in simultaneous equation models
... A l te r na t i v e l y we can examine the ratio L ( y q )/L (y), where Yg is the true value, and show that it is almost e verywhere grea te r than one and that a c c o r di n g l y the MLE must converge to this value. ...
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Stochastic Nonlinear Gompertz Model of Tumour Growth
... For comparison, we consider a therapy in which the dose is linearly increasing in time (Albano and Giorno, 2006). This can be implemented by sim- ply replacing the nonlinear tumour regression rate A 3 h ψ ...
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Reconstruction of Temporal Images by Gradient based Sequential Prediction
... Therefore nonlinear regression prediction model such as gradient adjusted temporal prediction procedure is applied to predict a temporal image for detecting the types of changes have occurred and is ...
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A nonlinear regression program with emphasis on the curvature and experimental design
... Program NLIN-CUR·ED (a nonlinear regression program with emphasis on the curvature and experimental design) is designed to facilitate" the choice of design points for experiments in [r] ...
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Intellectual capital and its determinants in Italian listed companies
... two regression models: a logistic regression model and a Tobit regression model in which the dependent variable is the market to book ratio and the independent variables are potential ...
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Some new proposed ridge parameters for the logistic regression model
... The parameter estimation method that based on the minimum residual sum of squares is unsatisfactory in the presence of multicollinearity. In (1970) Hoerl and Kennard introduced an alternative estimation approach which is ...
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Density in a censored regression model.
... Thus, Q in this case is the best approximation to f by a density satisfying the constraints in H.. The next section gives conditions under which e,h are identifiable and the expected log[r] ...
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