scholarly journals A Monograph on Nonlinear Regression Models

2018 ◽  
Vol 7 (4.10) ◽  
pp. 543
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
C. Narayana ◽  
J. Ravi sankar ◽  
P. Balasiddamuni
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Nonlinear Regression ◽  
Nonlinear Models ◽  
Nonlinear Least Squares ◽  
Error Variance ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Nonlinear Regression Model ◽  
Nonlinear Regression Models

This research article uses Matrix Calculus techniques to study least squares application of nonlinear regression model, sampling distributions of nonlinear least squares estimators of regression parametric vector and error variance and testing of general nonlinear hypothesis on parameters of nonlinear regression model. Arthipova Irina et.al [1], in this paper, discussed some examples of different nonlinear models and the application of OLS (Ordinary Least Squares). MA Tabati et.al (2), proposed a robust alternative technique to OLS nonlinear regression method which provide accurate parameter estimates when outliers and/or influential observations are present. Xu Zheng et.al [3] presented new parametric tests for heteroscedasticity in nonlinear and nonparametric models.  

Early LOC Estimation of Web Apps Created Using Yii Framework by Nonlinear Regression Models

2021 ◽  
Vol 20 ◽  
pp. 321-328
Author(s):  
Sergiy Prykhodko ◽  
Ivan Shutko ◽  
Andrii Prykhodko
Keyword(s):  
Regression Model ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Web Applications ◽  
Nonlinear Regression Model ◽  
Nonlinear Regression Models ◽  
Normalizing Transformations ◽  
Box Cox Transformation ◽  
Decimal Logarithm

We have performed early LOC estimation of Web applications (apps) created using the Yii framework by three nonlinear regression models with three predictors based on the normalizing transformations. We used two univariate transformations (the decimal logarithm and the Box-Cox transformation) and the Box-Cox four-variate transformation for constructing nonlinear regression models. The nonlinear regression model constructed by the Box-Cox four-variate transformation has better size prediction results compared to other regression ones based on the univariate transformations.

scholarly journals On the regularization of singular c-optimal designs

2009 ◽  
Vol 59 (5) ◽  
Author(s):  
Luc Pronzato
Keyword(s):  
Optimal Design ◽  
Asymptotic Normality ◽  
Regression Model ◽  
Least Squares ◽  
Nonlinear Regression ◽  
Scalar Function ◽  
Optimal Designs ◽  
Nonlinear Regression Model ◽  
True Value ◽  
Finite Space

AbstractWe consider the design of c-optimal experiments for the estimation of a scalar function h(θ) of the parameters θ in a nonlinear regression model. A c-optimal design ξ* may be singular, and we derive conditions ensuring the asymptotic normality of the Least-Squares estimator of h(θ) for a singular design over a finite space. As illustrated by an example, the singular designs for which asymptotic normality holds typically depend on the unknown true value of θ, which makes singular c-optimal designs of no practical use in nonlinear situations. Some simple alternatives are then suggested for constructing nonsingular designs that approach a c-optimal design under some conditions.

scholarly journals Quantile regression of nonlinear models to describe different levels of dry matter accumulation in garlic plants

2018 ◽  
Vol 48 (1) ◽  
Author(s):  
Guilherme Alves Puiatti ◽  
Paulo Roberto Cecon ◽  
Moysés Nascimento ◽  
Ana Carolina Campana Nascimento ◽  
Antônio Policarpo Souza Carneiro ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Dry Matter ◽  
Nonlinear Models ◽  
Development Stage ◽  
Dry Matter Accumulation ◽  
Nonlinear Regression Models ◽  
Biological Interpretation ◽  
Quantile Estimates

ABSTRACT: Plant growth analyses are important because they generate information on the demand and necessary care for each development stage of a plant. Nonlinear regression models are appropriate for the description of curves of growth, since they include parameters with practical biological interpretation. However, these models present information in terms of the conditional mean, and they are subject to problems in the adjustment caused by possible outliers or asymmetry in the distribution of the data. Quantile regression can solve these problems, and it allows the estimation of different quantiles, generating more complete and robust results. The objective of this research was to adjust a nonlinear quantile regression model for the study of dry matter accumulation in garlic plants (Allium sativum L.) over time, estimating parameters at three different quantiles and classifying each garlic accession according to its growth rate and asymptotic weight. The nonlinear regression model fitted was a Logistic model, and 30 garlic accessions were evaluated. These 30 accessions were divided based on the model with the closest quantile estimates; 12 accessions were classified as of lesser interest for planting, 6 were classified as intermediate, and 12 were classified as of greater interest for planting.

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