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Refers to a general estimation technique that selects the parameter value to minimize the squared difference between two quantities, such as the observed value of a variable, and the expected value of that observation conditioned on the parameter value. Gaussian linear models are fit by least squares and least squares is the idea underlying the use of mean-squared-error (MSE) as a way of evaluating an estimator.

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Weighted least squares fit with measurement uncertainty

I work with geophysics and in my research, some processes are described by power laws $$k=\gamma v^{\alpha} u^{\beta}$$ To find the indices $(\alpha,\beta)$ the equation is rewritten as a logarithm an …
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