Results 21 to 30 of about 15,886 (254)
Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high‐throughput phenotyping strategies enable a better connection between genotype and phenotype.
Jinyu Wang +7 more
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Fast Adaptive Penalized Splines
This article proposes a numerically simple method for locally adaptive smoothing. The heterogeneous regression function is modeled as a penalized spline with a varying smoothing parameter modeled as another penalized spline. This is formulated as a hierarchical mixed model, with spline coefficients following zero mean normal distribution with a smooth ...
Krivobokova, Tatyana +2 more
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Bootstrapping for Penalized Spline Regression [PDF]
We describe and contrast several different bootstrap procedures for penalized spline smoothers. The bootstrap methods considered are variations on existing methods, developed under two different probabilistic frameworks. Under the first framework, penalized spline regression is considered as an estimation technique to find an unknown smooth function ...
Kauermann, Göran +2 more
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Bayesian Analysis for Penalized Spline Regression Using WinBUGS
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression ...
Ciprian M. Crainiceanu +2 more
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Reproduction capabilities of penalized hyperbolic-polynomial splines
This paper investigates two important analytical properties of hyperbolic-polynomial penalized splines, HP-splines for short. HP-splines, obtained by combining a special type of difference penalty with hyperbolic-polynomial B-splines (HB-splines), were recently introduced by the authors as a generalization of P-splines. HB-splines are bell-shaped basis
Rosanna Campagna, Costanza Conti
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The Effect of the Outliers and Leverage Points in the Construction of the Bayesian and Bootstrap Confidence Intervals [PDF]
The aim of this research is to compare the bootstrap confidence intervals with the Bayesian confidence intervals for smoothing splines as well as the traditional confidence intervals to determine which of these limits are best in the presence of Outliers
Muzahim Mohammed
doaj +1 more source
Direct Determination of Smoothing Parameter for Penalized Spline Regression
Penalized spline estimator is one of the useful smoothing methods. To construct the estimator, having goodness of fit and smoothness, the smoothing parameter should be appropriately selected. The purpose of this paper is to select the smoothing parameter
Takuma Yoshida
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Penalized wavelets: Embedding wavelets into semiparametric regression [PDF]
We introduce the concept of penalized wavelets to facilitate seamless embedding of wavelets into semiparametric regression models. In particular, we show that penalized wavelets are analogous to penalized splines; the latter being the established ...
Ormerod, JT, Wand, MP
core +1 more source
Uniform convergence of penalized splines
Penalized splines are popular for nonparametric regression. We establish the minimax rate optimality of penalized splines for uniform convergence, thus improving the existing rate in the literature. The result is applicable to several types of penalized splines that are commonly used and holds under mild conditions on the design points.
Luo Xiao, Zhe Nan
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Smoothness Selection for Penalized Quantile Regression Splines [PDF]
Modern data-rich analyses may call for fitting a large number of nonparametric quantile regressions. For example, growth charts may be constructed for each of a collection of variables, to identify those for which individuals with a disorder tend to fall in the tails of their age-specific distribution; such variables might serve as developmental ...
Philip T, Reiss, Lei, Huang
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