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Titlebook: Smoothing Spline ANOVA Models; Chong Gu Book 2013Latest edition Springer Science+Business Media New York 2013 ANOVA.ANOVA models.Spline sm

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發(fā)表于 2025-3-21 17:05:37 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Smoothing Spline ANOVA Models
編輯Chong Gu
視頻videohttp://file.papertrans.cn/870/869161/869161.mp4
概述Covers latest research of smoothing methods in data analysis.Second edition is updated with latest computational methods, including the uses ofthe R package gss.Empirical studies are expanded, reorgan
叢書(shū)名稱(chēng)Springer Series in Statistics
圖書(shū)封面Titlebook: Smoothing Spline ANOVA Models; Chong Gu Book 2013Latest edition Springer Science+Business Media New York 2013 ANOVA.ANOVA models.Spline sm
描述.Nonparametric function estimation with stochastic data, otherwise.known as smoothing, has been studied by several generations of.statisticians. Assisted by the ample computing power in today‘s.servers, desktops, and laptops, smoothing methods have been finding.their ways into everyday data analysis by practitioners. While scores.of methods have proved successful for univariate smoothing, ones.practical in multivariate settings number far less. Smoothing spline.ANOVA models are a versatile family of smoothing methods derived.through roughness penalties, that are suitable for both univariate and.multivariate problems..In this book, the author presents a treatise on penalty smoothing.under a unified framework. Methods are developed for (i) regression.with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a.variety of sampling schemes; and (iii) hazard rate estimation with.censored life time data and covariates. The unifying themes are the.general penalized likelihood method and the construction of.multivariate models with built-in ANOVA decompositions. Extensive.discussions are devoted to model constructi
出版日期Book 2013Latest edition
關(guān)鍵詞ANOVA; ANOVA models; Spline smoothing; nonparametric smoothing; smoothing methods
版次2
doihttps://doi.org/10.1007/978-1-4614-5369-7
isbn_softcover978-1-4899-8984-0
isbn_ebook978-1-4614-5369-7Series ISSN 0172-7397 Series E-ISSN 2197-568X
issn_series 0172-7397
copyrightSpringer Science+Business Media New York 2013
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:27:12 | 只看該作者
Book 2013Latest editionted by the ample computing power in today‘s.servers, desktops, and laptops, smoothing methods have been finding.their ways into everyday data analysis by practitioners. While scores.of methods have proved successful for univariate smoothing, ones.practical in multivariate settings number far less. S
板凳
發(fā)表于 2025-3-22 00:24:26 | 只看該作者
Introduction,emes, there exist scores of nonparametric or semiparametric models, of which most are also known as smoothing methods. A family of such nonparametric models in a variety of stochastic settings can be derived through the penalized likelihood method, forming the subject of this book.
地板
發(fā)表于 2025-3-22 07:39:24 | 只看該作者
Introduction,nstraints, help to reduce noise but are responsible for “biases.” Representing the two extremes on the spectrum of “bias-variance” trade-off are standard parametric models and constraint-free nonparametric “models” such as the empirical distribution for a probability density. In between the two extr
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Regression with Correlated Responses,dentifiable from each other, the correlation can not be arbitrary but structured around a limited number of parameters, say γ, and the correlation structure should not be dependent on the covariate .. Of primary interest is the selection of tuning parameters, which now consist of the smoothing param
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發(fā)表于 2025-3-22 22:16:39 | 只看該作者
Probability Density Estimation,he computation of the estimates, and the asymptotic behavior of the estimates. Variants of (1.5) are also called for to accommodate samples subject to selection bias and samples from conditional distributions.
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Smoothing Spline ANOVA Models978-1-4614-5369-7Series ISSN 0172-7397 Series E-ISSN 2197-568X
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