ESTIMATION OF PARAMETERS OF A WEIBULL-TYPE NONLINEAR REGRESSION MODEL

2016 ◽  
Vol 49 (6) ◽  
pp. 409-416
Author(s):  
Trijya Singh
Keyword(s):  
Regression Model ◽  
Nonlinear Regression ◽  
Nonlinear Regression Model ◽  
Estimation Of Parameters

scholarly journals A Memoir on Nonlinear Regression Model and its Pseudo Model

2018 ◽  
Vol 7 (4.10) ◽  
pp. 995
Author(s):  
B. Mahaboob ◽  
J. PeterPraveen ◽  
J. Ravi Sankar ◽  
B. Venkateswarlu ◽  
C. Narayana
Keyword(s):  
Regression Model ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Applied Mathematics ◽  
Second Order ◽  
Nonlinear Regression Model ◽  
Linear Regression Models ◽  
Estimation Of Parameters ◽  
Nonlinear Regression Models ◽  
Pseudo Second Order

The main objective of this article is to specify a nonlinear regression model, formulate the assumptions on them and aquire its linear pseudo model. A model may be considered a mathematical description of a physical, chemical or biological state or process. Many models used in applied mathematics and Mathematical statistics are nonlinear in nature one of the major topics in the literature of theoretical and applied mathematics is the estimation of parameters of nonlinear regression models. A perfect model may have to many parameters to be useful. Nonlinear regression models have been intensively studied in the last three decades. Junxiong Lin et.al [1] , in their paper, compared best –fit equations of linear and nonlinear  forms of two widely used kinetic models, namely pseudo-first order and pseudo=second-order equations. K. Vasanth kumar [2], in his paper, proposed five distinct models of second order pseudo expression and examined a comparative study between method of least squares for linear regression models and a trial and error nonlinear regression procedures of deriving pseudo second order rare kinetic parameters. Michael G.B. Blum et.al [3] proposed a new method which fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics and then adaptively improves estimation using importance sampling.  

scholarly journals A Discourse on the Estimation of Nonlinear Regression Model

2018 ◽  
Vol 7 (4.10) ◽  
pp. 992
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
J. Ravi Sankar ◽  
J. Peter Praveen ◽  
C. Narayana
Keyword(s):  
Regression Model ◽  
Nonlinear Regression ◽  
Asymptotic Theory ◽  
Pso Algorithm ◽  
Nonlinear Regression Model ◽  
Time Varying ◽  
Estimation Of Parameters ◽  
Research Article ◽  
Regression Functions ◽  
Heteroscedastic Errors

The present study evaluates an estimation for regression model which are nonlinear with Goldfeld, Quandt and exponential structure for heteroscedastic errors. An IENLGLS (Iterative Estimated Nonlinear Generalised Least Squares) estimator based on Goldfeld and Quandt for parametric vector has been derived in this research article. Volkan   Soner Ozsoy e.t.al [1], in their paper, proposed an effective approach based on the particle Swarm Optimisation (PSO) algorithm in order to enhance the accuracy in the estimation of parameters of nonlinear regression model. Ting Zhang et.al [2], in their article, established an asymptotic theory for estimates of the time-varying regression functions. Felix Chan et.al [3], in their paper, proposed some principals which are sufficient for asymptotic normality and consistency of the MLH estimator 

scholarly journals Dimension Analysis-Based Model for Prediction of Shale Compressive Strength

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangyu Fan ◽  
Fenglin Xu ◽  
Lin Chen ◽  
Qiao Chen ◽  
Zhiwei Liu ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Prediction Error ◽  
Diameter Ratio ◽  
Nonlinear Regression Model ◽  
Average Prediction Error ◽  
Model Average ◽  
Dimension Analysis ◽  
Longmaxi Shale

The compressive strength of shale is a comprehensive index for evaluating the shale strength, which is linked to shale well borehole stability. Based on correlation analysis between factors (confining stress, height/diameter ratio, bedding angle, and porosity) and shale compressive strength (Longmaxi Shale in Sichuan Basin, China), we develop a dimension analysis-based model for prediction of shale compressive strength. A nonlinear-regression model is used for comparison. A multitraining method is used to achieve reliability of model prediction. The results show that, compared to a multi-nonlinear-regression model (average prediction error = 19.5%), the average prediction error of the dimension analysis-based model is 19.2%. More importantly, our dimension analysis-based model needs to determine only one parameter, whereas the multi-nonlinear-regression model needs to determine five. In addition, sensitivity analysis shows that height/diameter ratio has greater sensitivity to compressive strength than other factors.

Sign in / Sign up

Export Citation Format

Share Document