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- Author: M G Akritas
- Publisher: JAI Press
- ISBN-10: 0444513787
- ISBN-13: 9780444513786
- Format: 17.8 x 24.4 x 2.5 cm, kieti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Recent Advances and Trends in Nonparametric Statistics (e-book) (used book) | bookbook.eu
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Description
The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection of short articles - most of which having a review component - describing the state-of-the art of Nonparametric Statistics at the beginning of a new millennium.
Key features:
- algorithic approaches
- wavelets and nonlinear smoothers
- graphical methods and data mining
- biostatistics and bioinformatics
- bagging and boosting
- support vector machines
- resampling methods
EXTRA 10 % discount with code: EXTRA
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- Author: M G Akritas
- Publisher: JAI Press
- ISBN-10: 0444513787
- ISBN-13: 9780444513786
- Format: 17.8 x 24.4 x 2.5 cm, kieti viršeliai
- Language: English English
The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection of short articles - most of which having a review component - describing the state-of-the art of Nonparametric Statistics at the beginning of a new millennium.
Key features:
- algorithic approaches
- wavelets and nonlinear smoothers
- graphical methods and data mining
- biostatistics and bioinformatics
- bagging and boosting
- support vector machines
- resampling methods
Reviews