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- W4256548136 abstract "A new method of estimation of the smoothing parameters of the Aitchison and Aitken kernel method for categorical data is proposed. This is based on minimizing an unbiased estimator of the mean squared error, and it provides a natural approach to multivariate estimation, thus allowing for the interaction structure of data in a simple way. The kernel smoothing technique is shown theoretically to offer a potential improvement over the direct use of cell frequencies, by means of an existence theorem akin to that used by Hoerl and Kennard for ridge regression. The proposed smoothing parameter estimates are compared with competing methods by means of a simulation in the simple binomial case and are illustrated on a set of multivariate data used in Titterington (1980). A property of invariance to the reversal of estimate formation and marginalization is proved for any multivariate kernel and estimation procedure." @default.
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- W4256548136 date "1985-08-01" @default.
- W4256548136 modified "2023-10-01" @default.
- W4256548136 title "Kernel Estimates for Categorical Data" @default.
- W4256548136 doi "https://doi.org/10.2307/1269711" @default.
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