Abstract
Optimized moving local regression is an extension of Cleveland’s loess technique that takes a suspected misspecification of the model into account. The weights are chosen so that the effect of the misspecification is minimized. The derivation of optimal weights is shown to be similar to that of an optimal design for an experiment.
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References
Cleveland, W.S. (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74, 829836.
Fedorov, V.V., Hack!, P., and Müller, W.G. (1993b). Optimized Moving Local Regression. Another Approach to Forecasting, pp. 137–144 in Müller, W.G., Wynn, H.P., and Zhigljaysky, A.A. (eds.), Model-Oriented Data Analysis. Heidelberg: Physica-Verlag.
Müller, W.G. (1992). The Evaluation of Bank Accounts Using Optimized Moving Local Regressions, pp. 145–150 in Fahrmeir, L., Francis, B., Gilchrist, R., and Tutz, G. (eds.), Advances in GLIM and Statistical Modelling. Berlin: Springer-Verlag.
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© 1994 Springer-Verlag Berlin Heidelberg
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Fedorov, V., Hackl, P., Müller, W.G. (1994). Optimized Local Regression: Computational Methods for the Moving Average Case. In: Dutter, R., Grossmann, W. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-52463-9_58
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DOI: https://doi.org/10.1007/978-3-642-52463-9_58
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0793-6
Online ISBN: 978-3-642-52463-9
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