Strong consistency of least-squares estimates in regression models.
A general theorem on the limiting behavior of certain weighted sums of i.i.d. random variables is obtained. This theorem is then applied to prove the strong consistency of least-squares estimates in linear and nonlinear regression models with i.i.d. errors under minimal assumptions on the design and weak moment conditions on the errors.
Department of Mathematical Statistics, Columbia University, New York, New York 10027.
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Lai
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Robbins