Results 241 to 250 of about 6,450 (280)

The use of spline, Bayesian spline and penalized Bayesian spline regression for modeling

, 2013
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Capturing infant and child growth dynamics with P-splines mixed effects models

Hernandez MA, Li Z, Cole TJ, Ong YY, Tilling K, Elhakeem A.   +5 more
europepmc   +1 more source

Neutrophil-to-high-density lipoprotein cholesterol ratio as a predictor of outcomes after successful endovascular reperfusion in acute ischemic stroke. [PDF]

Front Neurol
Xu R, Weng M, Lei Q, Liu Z.
europepmc   +1 more source

Associations of inflammation-related nutritional and metabolic status indices CAR and CTI with 90-day unfavorable functional outcomes in patients with acute ischemic stroke. [PDF]

Front Nutr
Liu X, Zheng X, Chen Z, Chen H, Xie M, Zhang J.   +5 more
europepmc   +1 more source

Data-driven selection of the spline dimension in penalized spline regression

Biometrika, 2011
A number of criteria exist to select the penalty in penalized spline regression, but the selection of the number of spline basis functions has received much less attention in the literature. We propose a likelihood-based criterion to select the number of basis functions in penalized spline regression.
Göran Kauermann, Jean D. Opsomer
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On the asymptotics of penalized splines

Biometrika, 2008
SUMMAvRY We study the asymptotic behaviour of penalized spline estimators in the univariate case. We use B-splines and a penalty is placed on mth-order differences of the coefficients. The number of knots is assumed to converge to infinity as the sample size increases. We show that penalized splines behave similarly to Nadaraya-Watson kernel estimators
Yingxing Li, David Ruppert
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Adaptive penalized splines for data smoothing

Computational Statistics & Data Analysis, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lian-Qiang Yang, Yongmiao Hong
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Constrained penalized splines

Canadian Journal of Statistics, 2012
AbstractThe penalized spline is a popular method for function estimation when the assumption of “smoothness” is valid. In this paper, methods for estimation and inference are proposed using penalized splines under additional constraints of shape, such as monotonicity or convexity.
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