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I've repeated Higuchi calculations and got precisely the same answer as he promised D=-1.5143. Pay close attention to the number of times he divides the series length Lmk by k. That was my mistake for the first time, when I've lost the final averaging between the set of k series and we moved from Lkm to <Lkm>. Here the pointy brackets stands for ...


Let $Y = X\beta +\epsilon$ be a multilinear regression model, where $Y = n\times 1$, $\beta=k\times 1$, $X = n\times k$ and $\epsilon$ is an error term. Then the least squares estimator, which minimizes the squared errors is $\hat\beta = (X^TX)^{-1}X^TY$. One can show that the maximum likelihood estimator is the same.

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