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We develop an approach to choosing optimal kernel weights for nonparametric estimation of the slope of the regression function which uses maximization of power, rather than minimization of integrated mean squared error (IMSE), as its optimality criterion. This power criterion leads to optimal kernel weights whose derivation is simpler than under other criteria and which provides an intuitive understanding of the nature of the optimality.
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