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02633nam a22005295i 4500 |
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978-0-387-22732-0 |
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DE-He213 |
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20151204141335.0 |
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cr nn 008mamaa |
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100301s1999 xxu| s |||| 0|eng d |
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|a 9780387227320
|9 978-0-387-22732-0
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|a 10.1007/b98858
|2 doi
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|d GrThAP
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|a QA273.A1-274.9
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|a QA274-274.9
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|a PBT
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|a MAT029000
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|a 519.2
|2 23
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|a Loader, Clive.
|e author.
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|a Local Regression and Likelihood
|h [electronic resource] /
|c by Clive Loader.
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|a New York, NY :
|b Springer New York,
|c 1999.
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|a XIV, 290 p.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
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|a text file
|b PDF
|2 rda
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|a Statistics and Computing,
|x 1431-8784
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|a The Origins of Local Regression -- Local Regression Methods -- Fitting with LOCFIT -- Local Likelihood Estimation -- Density Estimation -- Flexible Local Regression -- Survival and Failure Time Analysis -- Discrimination and Classification -- Variance Estimation and Goodness of Fit -- Bandwidth Selection -- Adaptive Parameter Choice -- Computational Methods -- Optimizing Local Regression.
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|a Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, and extensions to local likelihood and density estimation. Basic theoretical results and diagnostic tools such as cross validation are introduced along the way. Examples illustrate the implementation of the methods using the LOCFIT software.
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|a Mathematics.
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|a Economics, Mathematical.
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|a Probabilities.
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|a Statistics.
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|a Mathematics.
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|a Probability Theory and Stochastic Processes.
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|a Statistics and Computing/Statistics Programs.
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|a Statistics for Business/Economics/Mathematical Finance/Insurance.
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|a Quantitative Finance.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9780387987750
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|a Statistics and Computing,
|x 1431-8784
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|u http://dx.doi.org/10.1007/b98858
|z Full Text via HEAL-Link
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|a ZDB-2-SMA
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|a ZDB-2-BAE
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|a Mathematics and Statistics (Springer-11649)
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