Results 91 to 100 of about 15,953 (268)
The use of spline, Bayesian spline and penalized Bayesian spline regression for modeling
, 2021 Splayn, cezalandırılmış splayn ve Bayesyen splayn olarak adlandırılan parametrik olmayan regresyon yöntemleri modellemede esneklik ve sabit bir modele bağlı olmamak gibi büyük avantajlar sağlar. Cezalandırılmış splayn regresyon, parametrik olmayan splayn düzeltme düşüncesini kullanır.
openaire +2 more sources
Nonparametric inference in hidden Markov models using P-splines [PDF]
, 2013 Hidden Markov models (HMMs) are flexible time series models in which the distributions of the observations depend on unobserved serially correlated states.
Alexandrovich +25 more
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Quantitative Diffusion and T2 Mapping Using RF‐Modulated Phase‐Based Gradient Echo Imaging
Magnetic Resonance in Medicine, EarlyView.ABSTRACT Purpose To introduce and evaluate the feasibility of a novel RF‐phase modulated gradient echo (GRE) method for quantitative diffusion MRI, aimed at mitigating geometric distortion and enabling high‐resolution 3D quantitative diffusion/T2 mapping as a complementary alternative to conventional DWI.
Daiki Tamada +4 more
wiley +1 more source
Bayesian Analysis for Penalized Spline Regression Using WinBUGS [PDF]
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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Self-Modeling Regression with Random Effects Using Penalized Splines [PDF]
, 2000 20 pages, 1 article*Self-Modeling Regression with Random Effects Using Penalized Splines* (Altman, Naomi S.; Villarreal, Julio C.) 20 ...
Altman, Naomi S. +4 more
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Boosting Additive Models using Component-wise P-Splines [PDF]
, 2007 We consider an efficient approximation of Bühlmann & Yu’s L2Boosting algorithm with component-wise smoothing splines. Smoothing spline base-learners are replaced by P-spline base-learners which yield similar prediction errors but are more advantageous ...
Hothorn, Torsten, Schmid, Matthias
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Explicit‐Implicit Material Point Method for Dense Granular Flows With a Novel Regularized µ(I) Model
International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.ABSTRACT The material point method (MPM) is widely employed to simulate granular flows. Although explicit time integration is favored in most current MPM implementations for its simplicity, it cannot rigorously incorporate the incompressible µ(I)‐rheology, an efficient model ubiquitously adopted in other particle‐based numerical methods. While operator‐
Hang Feng, Zhen‐Yu Yin
wiley +1 more source
BMC Medical Research Methodology, 2017 Background Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial.
Nils Ternès +2 more
doaj +1 more source
Oikos, EarlyView.Functional diversity (FD) is an essential community property connecting biodiversity, ecosystem functioning, and conservation objectives. In agricultural landscapes, avian communities, which play key functional roles, are facing large‐scale biodiversity erosion, largely due to land‐use changes.
Pietro Tirozzi +3 more
wiley +1 more source
Exact asymptotics of the optimal Lp-error of asymmetric linear spline approximation
, 2013 In this paper we study the best asymmetric (sometimes also called penalized or sign-sensitive) approximation in the metrics of the space $L_p$, $1\leqslant p\leqslant\infty$, of functions $f\in C^2\left([0,1]^2\right)$ with nonnegative Hessian by ...
Babenko, Vladyslav +3 more
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