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Nonparametric Density Estimation

Part of the Springer Series in Statistics book series (SSS)

Abstract

Smoothing is one of the most fundamental techniques in nonparametric function estimation. It usually refers to one-dimensional scatterplot smoothing and density estimation. It serves as a useful building block for nonparametric estimation in a multidimensional setting. Smoothing arose first from spectral density estimation in time series. In a discussion of the seminal paper by Bartlett (1946), Henry E. Daniels suggested that a possible improvement on spectral density estimation could be made by smoothed periodograms. The theory and techniques were then systematically developed by Bartlett (1948, 1950). Thus, smoothing techniques were already prominently featured in time series analysis over half a century ago.

Keywords

Mean Square Error Kernel Function Density Estimation Kernel Density Kernel Density Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Sciences+Business Media, Inc. 2005

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