Fissure
发表于 2025-3-27 00:34:00
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.
critic
发表于 2025-3-27 02:01:03
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affluent
发表于 2025-3-27 08:58:50
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值得赞赏
发表于 2025-3-27 11:06:13
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.
削减
发表于 2025-3-27 15:42:36
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.
aspect
发表于 2025-3-27 19:51:24
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
背信
发表于 2025-3-27 22:03:35
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动机
发表于 2025-3-28 04:36:48
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涂掉
发表于 2025-3-28 08:34:16
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
Budget
发表于 2025-3-28 13:53:32
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