Abstract
Nonparametric techniques consist of sophisticated alternatives to traditional parametric models for studying multivariate data. What makes these alternative techniques so appealing to the data analyst is that they make no specific distributional assumptions and, thus, can be employed as an initial exploratory look at the data. In this chapter, we discuss methods for nonparametric estimation of a probability density function.
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© 2013 Springer Science+Business Media New York
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Izenman, A.J. (2013). Nonparametric Density Estimation. In: Modern Multivariate Statistical Techniques. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-78189-1_4
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DOI: https://doi.org/10.1007/978-0-387-78189-1_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-78188-4
Online ISBN: 978-0-387-78189-1
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