On Least Squares Linear Regression Without Second Moment

10/18/2017 ∙ by Rajeshwari Majumdar, et al. ∙ 0

If X and Y are real valued random variables such that the first moments of X, Y, and XY exist and the conditional expectation of Y given X is an affine function of X, then the intercept and slope of the conditional expectation equal the intercept and slope of the least squares linear regression function, even though Y may not have a finite second moment. As a consequence, the affine in X form of the conditional expectation and zero covariance imply mean independence.



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