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For Classical Regression Model the OLS or Ordinary Least Squares - estimators (or the betas) are BLUE (Best, Linear, Unbiased, Estimator) when :
- The regression is linear in the coefficients, it is correctly specified and has an additive error term.
- Mean of the error term is zero. (Include a constant term in the regression (B0 which will force the mean to be zero)
- The independent variables are not correlated with the error term. (If they are correlated then the betas will be biased.)
- Observations of the error term (the residuals) are not correlated with each other.
- The error term has a constant variance (Homoskedasticity)
- No independent variable is a perfect linear function of any of the other independent variable. (If this is true - multicollinearity will occur)
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What is the advantage of using OLS method?
OLS leads to a closed-form solution. The problems in other metrics such as L1 do not. Furthermore, the statistical theory for OLS is much richer than for other metrics in spite of the fact that OLS leads to difficulties in dealing with 'outliers'.
How does a traffic estimator operate?
Traffic estimators give you an estimate of how many readers you can expect to get with a published article. All of this depends on the type of keywords you use and how well you use them in your article. Most estimators will help you generate good keywords to put into your articles as well.
Maximum likelihood estimators of the logistic distribution?
The likelihood has to be maximized numerically, as the order statistic is minimal sufficient
What is the maximum likelihood estimator of the Cauchy distribution?
Maximum likelihood estimators of the Cauchy distribution cannot be written in closed form since they are given as the roots of higher-degree polynomials. Please see the link for details.
What is a BLUE rectangle?
A rectangle that is colored blue.
