Dear researchers,
It is very urgent to know for me the basic difference between OLS and maximum likelihood method.
The links below may help get some understanding
http://www.differencebetween.net/science/mathematics-statistics/differences-between-ols-and-mle/
http://stats.stackexchange.com/questions/143705/maximum-likelihood-method-vs-least-squares-method
1. “OLS” stands for “ordinary least squares” while “MLE” stands for “maximum likelihood estimation.”
2. The ordinary least squares, or OLS, can also be called the linear least squares. This is a method for approximately determining the unknown parameters located in a linear regression model.
3. Maximum likelihood estimation, or MLE, is a method used in estimating the parameters of a statistical model and for fitting a statistical model to data.
Hi.
It depends on the likelihood function.
for some parameters they are same and for some not.
good luck.
OLS: no assumption on the random error term of the linear model.
MLE:: randon error is assumed to follow certain distribution (usually follow a normal distribution).
Thus; parameters of the lianear model can determined. Ok, good luck.
The MLE is concerned about choosing the parameter which can maximize the likelihood or equivalently the log-likelihood function. And then fit the model based on the trial estimated parameter value and calculate the mean of the model. To find the iterative weighted and working dependence and based on this two and the design matrix we can estimate the best parameter value.
The OLS will take the parameter value which minimize the error ( residuals) of the model. It will take into acount that the residual sum of square and derivate with respect to the parameter regression coefficient (beta) and set it to zero and then we will find the parameter value which minimize the error(residual sum of square).
Dear,
The ordinary least squares, or OLS is a method for approximately determining the unknown parameters located in a linear regression model. This method is obtained by minimizing the total of squared vertical distances between the observed responses within the dataset and the responses predicted by the linear approximation. Through a simple formula, you can express the resulting estimator, especially the single regressor, located on the right-hand side of the linear regression model. Also it is your overall solution in minimizing the sum of the squares of errors in your equation.
The Maximum likelihood Estimation, or MLE, is a method used in estimating the parameters of a statistical model, and for fitting a statistical model to data. Using the maximum likelihood estimation, you can estimate the mean and variance of the height of your subjects. The MLE would set the mean and variance as parameters in determining the specific parametric values in a given model.
Also for more information about this subject please see links and attched file in this topics.
-What is the difference between OLS and MLE? - Quora
https://www.quora.com/What-is-the-difference-between-OLS-and-...
-regression - Maximum likelihood method vs. least squares method ...
https://stats.stackexchange.com/.../maximum-likelihood-method-vs...
-Applied Multiple Regression/Correlation Analysis for the Behavioral ...
https://books.google.dz/books?isbn=1134801017
-Socioeconomic Differences in Old Age Mortality
https://books.google.dz/books?isbn=140208692X
Best regards
Dear Group Members, could anyone please explain the.... Available from: https://www.researchgate.net/post/Dear_Group_Members_could_anyone_please_explain_the_differences_b_w_OLS_Maximum_Likelihood_Estimation [accessed Aug 4, 2017].
Dear Rima Houari Saoudi,
Thanks a lot for your answer. I hope, it will help me to be clear about the ideas.
Wish you have a good day.
Best Regards,
Bezon Kumar
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