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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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

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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.

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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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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