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Differences between OLS and MLE

Differences between OLS and MLE

Though they may sound similar, OLS ordinary least square and MLE maximum likelihood estimators are two different types of estimation techniques. In this blog post we’ll explore the differences between these two methods and provide examples of when each is most appropriate. We’ll also take a look at how to choose the right estimator for your data set. So, let’s dive in!

What is OLS?

OLS is a statistical method used to estimate the parameters of a linear regression model. OLS selects the values of the parameters that minimize the sum of the squared residuals. In other words, OLS finds the line that best fits the data. OLS is a powerful tool that can be used to answer a variety of research questions. For example, OLS can be used to estimate the effect of a policy change on economic outcomes. OLS can also be used to examine relationships between variables, such as the relationship between income and health. OLS is a versatile tool that is widely used in economics and other social sciences.

What is MLE?

MLE is a statistical technique that is used to estimate the parameters of a probability distribution based on a set of data. The MLE estimate is the value of the parameter that is most likely to have generated the data. MLE can be used to estimate the parameters of both discrete and continuous distributions. For example, MLE could be used to estimate the mean and variance of a Normal distribution based on a set of data. MLE is one of the most popular estimation methods because it has several desirable properties, including consistency and asymptotic efficiency.

Differences between OLS and MLE

OLS and MLE are two methods of estimation that are used in statistics. OLS is a method of estimating the parameters of a linear regression model, while MLE is a method of estimating the parameters of a probabilistic model. OLS is a more efficient method of estimation when the data are normally distributed, while MLE is more efficient when the data are not normally distributed. OLS is also more robust to outliers, while MLE is more sensitive to outliers. In general, OLS is preferred when the data are well-behaved, while MLE is preferred when the data are not well-behaved.

Conclusion

There are many different ways to estimate a model, each with its own strengths and weaknesses. In this blog post, we’ve explored the differences between OLS ordinary least squares and MLE maximum likelihood estimators. We’ve shown that the MLE is more accurate than the OLS in most cases and is better at dealing with nonlinearity in the data.

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