nonlinear regression
The Thermodynamic Dissociation Constants of Azathioprine by the Nonlinear Regression and Factor Analysis of Multiwavelength Spectrophotometric pH Titration Data
... The mixed dissociation constant of azathioprine—chemically 6-(3-methyl-5-nitroimidazol-4-yl)sulfanyl-7H- purine at various ionic strengths I of range 0.01-0.2, and at temperatures of 25℃ and 37℃, was determined with the ...
11
A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees
... study nonlinear regression of deterministic signals using trees, where the space of regressors is partitioned using a nested tree structure where separate regressors are assigned to each re- ...final ...
16
Estimating physiological tolerances – a comparison of traditional approaches to nonlinear regression techniques
... when more sophisticated analyses were less available, but BSR makes a number of unsupported assumptions about the underlying form of the relationship between the rate of oxygen consumption and oxygen availability. The ...
7
A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error
... Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression ...
22
Nonlinear Regression Estimation Using Subset-Based Kernel Principal Components
... In this section, we use three simulated examples to illustrate the finite sample performance of the proposed subset-based KPCA method and compare it with the global KPCA and other existing nonparametric estimation ...
36
Bayesian nonlinear regression for large p small n problems
... multivariate nonlinear regression models in this setup for accurate ...vector regression model with Vapnik’s ϵ -insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the ...
13
Evaluation of parameter uncertainties in nonlinear regression using Microsoft Excel Spreadsheet
... linear regression, but not in nonlinear regression. Nonlinear regression pro- grams usually give the parameter uncertainty by calculating the standard error of the mean, and assuming ...
12
CBPS Based Inference in Nonlinear Regression Models with Missing Data
... In this article, to improve the doubly robust estimator, the nonlinear regression models with missing responses are studied. Based on the covariate balancing propensity score (CBPS), estima- tors for the ...
11
Identification and estimation of nonlinear regression models using control functions
... Chapter three develops a test for monotonicity of a (possibly nonlinear) regression function under endogeneity. The novel testing framework is applied to study mono- tonicity of the reservation wage as a ...
118
A Technique for Estimator Densities applied to Exponential Regression, Nonlinear Regression models and Biochemical Titration Curves
... ing the method described in [8] for a nonlinear regression model that is based on equation (17), with two estimable parameters and homoscedastic independent normal er- rors. The fit of (21) was assessed on ...
8
Local Curvature and Centering Effects in Nonlinear Regression Models
... p-dimensional nonlinear regression surface defines a local frame of reference for inference with the intrinsic curvature and even simple centering has effects in nonlinear models both generally and ...
9
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 ...
11
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] ...
27
Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles
... linearize nonlinear regression quantiles in a nonlinear regression model with long range dependent errors , where the design variables can be either random or nonrandom + These , in turn , ...
23
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] ...
16
A Preliminary Performance Study on Nonlinear Regression Models using the Jaya Optimisation Algorithm
... of nonlinear regression parameters is that it produces reliable and high quality results for the RSS values and parameter estimates of the regression models with less computational effort (or time) ...
5
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 ...
8
A simplified nonlinear regression method for human height estimation in video surveillance
... Figure 2 shows the camera calibration process using the proposed method. First, head and foot points were marked and saved, as shown in Fig. 2a. Initial values of the focal length, the tilt angle, and the camera height ...
9
Nonlinear regression without i.i.d. assumption
... To overcome this difficulty, we use the max-mean as the loss function. First, we split the training set into several groups and in each group the i.i.d. condition can be satisfied. Then, the average loss is used for each ...
15
Analysis of Flight Fuel Consumption Based on Nonlinear Regression
... Abstract. For to increase fuel utilization and reduce carbon emissions of flights, the relationship between each factor and fuel consumption was established using real flight data and nonlinear regression ...
14