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cr0001铁杆木虫 (著名写手)
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Smooth principle of Loess algorithm as follows: yi=g(xi)+ εi where g is the regression function and εi is a random error. The idea of local regression is that near x = x0, the regression function g(x) can be locally approximated by the value of a function in some specified parametric class. Such a local approximation is obtained by fitting a regression surface to the data points within a chosen neighborhood of the point x0. In this method, weighed least squares are used to fit quadratic functions of the predictors at the centers of the neighborhoods. The radius of each neighborhood is chosen so that the neighborhood contains a specified percentage of the data points. The fraction of the data, called the smoothing parameter, in each local neighborhood is weighted by a smooth decreasing function of their distance from the center of the neighborhood. |
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