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That is incorrect. Least squares follows directly from the central limit theorem.
by xadhominemx 1y ago
That is incorrect. Least squares follows directly from the central limit theorem.
- jampekka 1y agoCentral limit theorem tells in practice that gaussian distributions is can be expected to be quite common. And it makes the gaussian distribution a good first guess. Least squares gives the ML estimate for gaussian residuals. I don't find this very direct, and there being a rationale doesn't mean that rationale is what in reality drives the usage. I mention the relation to the gaussian distribution. Which part of the comment is incorrect?
- xadhominemx 1y agoThis part is incorrect: “ The main practical reason why square error is minimized in ordinary linear regression is that it has an analytical solution” OLS is popular because it gives correct answers as a result of the CLT
- disgruntledphd2 1y agoAnd it has an analytical solution, which was important before computing (and still makes it quicker today).
- kyllo 1y agoIn other words, as economists say, because OLS is provably the BLUE (Best Linear Unbiased Estimator) aka the Gauss-Markov Theorem.