Titlebook: Maximum Penalized Likelihood Estimation; Volume II: Regressio Vincent N. LaRiccia,Paul P.‘Eggermont Book 2009 Springer-Verlag New York 2009

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Nonparametric Regression,blems that may be emulated in other settings. Here, in the introductory chapter, we loosely survey what we will be doing and which standard topics will be omitted. However, before doing that, it is worthwhile to describe various problems in which the need for nonparametric regression arises.

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Kalman Filtering for Spline Smoothing, GCV functionals by way of the Kalman filter should not be forgotten. Things already get dicey when justifying the GML method for choosing the smoothing parameter, but it must be admitted that this seems to work very well (see Chapter 23). However, the authors draw the line at Bayesian confidence bands for the unknown regression function.

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Equivalent Kernels for Smoothing Splines,weaker interpretation all “good” nonparametric regression estimators would be equivalent to one another.) An interesting twist in the “equivalent” kernel story is that the bias and the noise components require different representations for the “equivalence” to hold in the strict sense.

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Nonparametric Regression, independent normal random variables with mean 0 and unknown variance σ.. The object is to estimate the (smooth) function .. and construct inferential procedures regarding the model (1.2). However, the real purpose of this volume is to outline a down-to-earth approach to nonparametric regression pro

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Sieves,bspaces of the ambient .. space. This is somewhat different from the alternative interpretation of a sieve as a nested sequence of compact subsets of the .. space; see § 12.2. Either way, a sieved estimator is defined as the solution to a minimization problem, e.g., least-squares or maximum likeliho

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