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- DescriptionThis book deals with parametric and nparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
- Author(s)Paul P. Eggermont,Vincent N. LaRiccia
- PublisherSpringer-Verlag New York Inc.
- Date of Publication03/12/2010
- Series TitleSpringer Series in Statistics
- Place of PublicationNew York, NY
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Content Noteblack & white illustrations
- Weight807 g
- Width156 mm
- Height234 mm
- Spine27 mm
- Format DetailsTrade paperback (US)
- Edition StatementSoftcover reprint of hardcover 1st ed. 2001
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