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IJSTR >> Volume 8 - Issue 11, November 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Bias In The Maximum Likelihood Estimation Of Parameters Of Nonlinear Regression Models

[Full Text]



B. Mahaboob, B.Venkateswarlu, C. Narayana, M. Sivaiah



Bias, MLE(Maximum Likelihood Estimation), Multivariate Normal distribution, Variance-Covariance Matrix, Residual Vector, Linearity of Regression, Rank correlation coefficient.



Nonlinear model building has become an important tool in Predictive Analysis and Forecasting Theory. MLE is a phenomenon in which one can obtain unknown coefficients of a distribution by optimizing a likelihood function. Maximum likelihood estimate is the vector in parameter space which optimizes the likelihood function. This research article throws a light on the BIAS in the MLE of unknown coefficients of statistical models which are not linear. In addition to this a test for the linearity of regression has been proposed. If the ML function possesses derivatives one can apply first derivative test to obtain optimum values. But in some situations the equations of first degree of ML function are to be solved in explicit manner. For example in linear statistical model OLS estimator optimize the ML function. In vast number of cases advanced numerical techniques should be implemented in order to get ML function. As the application of ML technique is both flexible and intuitive this technique has become an indispensable tool in statistical inference.



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