scholarly journals Maximum likelihood estimation of nonlinear regression models with Berkson measurement errors

2016 ◽  
pp. 88-98
Author(s):  
Anastasiia Timofeeva ◽  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Nonlinear Regression ◽  
Measurement Errors ◽  
Regression Models ◽  
Likelihood Estimation ◽  
Nonlinear Regression Models

Corrected maximum-likelihood estimation in a class of symmetric nonlinear regression models

2000 ◽  
Vol 46 (4) ◽  
pp. 317-328 ◽  
Author(s):  
Gauss M. Cordeiro ◽  
Silvia L.P. Ferrari ◽  
Miguel A. Uribe-Opazo ◽  
Klaus L.P. Vasconcellos
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Likelihood Estimation ◽  
Nonlinear Regression Models

scholarly journals Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Pierre Nguimkeu
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Moving Average ◽  
Asymptotic Properties ◽  
Likelihood Estimation ◽  
Small Sample ◽  
First Order ◽  
Wald Tests ◽  
Nonlinear Regression Models ◽  
Mobile Cellular

This paper proposes an improved likelihood-based method to test for first-order moving average in the disturbances of nonlinear regression models. The proposed method has a third-order distributional accuracy which makes it particularly attractive for inference in small sample sizes models. Compared to the commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samples and asymptotic properties of the maximum likelihood estimation, the proposed method has remarkable accuracy. Monte Carlo simulations are provided to show how the proposed method outperforms the existing ones. Two empirical examples including a power regression model of aggregate consumption and a Gompertz growth model of mobile cellular usage in the US are presented to illustrate the implementation and usefulness of the proposed method in practice.

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