Results 231 to 240 of about 56,202 (281)

Integration of finite element method and artificial intelligence for evaluating PEEK composites in rib cage reconstruction process under impact conditions. [PDF]

J Mater Sci Mater Med
Shash YH, Elden RH.
europepmc   +1 more source

A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. [PDF]

PLoS Negl Trop Dis
Nyarko E, Agyemang EF, Ameho EK, Agyekum L, Gutiérrez JM, Fernandez EA.   +5 more
europepmc   +1 more source

Exploring the relative contribution of genetic and external exposomic risk scores to allergies in elderly women. [PDF]

Sci Rep
Reyes PWC, Wigmann C, Kress S, Schwender H, Schikowski T.   +4 more
europepmc   +1 more source

DCBAN: A Dynamic Confidence Bayesian Adaptive Network for Reconstructing Visual Images from fMRI Signals. [PDF]

Brain Sci
Wang W, Cai Y, Zhang R, Li J, Ye Z, Wang Z.   +5 more
europepmc   +1 more source

Effects of tobacco intercropping on the Spatial relationship between natural enemies and pests and economic efficiency. [PDF]

Sci Rep
Sun J, Ran Y, Mark G, Jin Y, Ding W.
europepmc   +1 more source

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Generalized ridge regression versus simple ridge regression for generation of kinetic parametric images in PET

1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019), 2003
Weighted linear regression (WLR) is computationally efficient for generating parametric images in dynamic PET studies. However, due to high noise level of pixel kinetics, parametric images estimated by WLR usually have high variability. The authors have shown earlier that, for image-wise model fitting, the incorporation of simple ridge regression and ...
null Yun Zhou, null Sung-Cheng Huang, null Yih-Ing Hser   +2 more
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Generalized ridge regression: a note on negative ridge parameters

Communications in Statistics - Theory and Methods, 1983
Generalized ridge regression is extended to include negative values of the ridge parameter. In doing so, classes of biased estimators and estimators obtained by variable selection proce¬dures can be formulated as ridge estimators having stochastic, possibly negative, ridge parameter values.
Tsushung A. Hua, Richard F. Gunst
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A Generalized Stochastic Restricted Ridge Regression Estimator

Communications in Statistics - Theory and Methods, 2014
In this article, we introduce a new stochastic restricted estimator for the unknown vector parameter in the linear regression model when stochastic linear restrictions on the parameters hold. We show that the new estimator is a generalization of the ordinary mixed estimator (OME), Liu estimator (LE), ordinary ridge estimator (ORR), (k-d) class ...
M. I. Alheety, B. M. Golam Kibria
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Optimization of Generalized $$C_p$$ Criterion for Selecting Ridge Parameters in Generalized Ridge Regression

2020
In a generalized ridge (GR) regression, since a GR estimator (GRE) depends on ridge parameters, it is important to select those parameters appropriately. Ridge parameters selected by minimizing the generalized \(C_p\) (\(GC_p\)) criterion can be obtained as closed forms.
Mineaki Ohishi, Hirokazu Yanagihara, Hirofumi Wakaki   +2 more
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The feasible generalized restricted ridge regression estimator

Journal of Statistical Computation and Simulation, 2016
ABSTRACTThe presence of autocorrelation in errors and multicollinearity among the regressors have undesirable effects on the least-squares regression. There are a wide range of methods which are proposed to overcome the usefulness of the ordinary least-squares estimator or the generalized least-squares estimator, such as the Stein-rule, restricted ...
Özbay N., Kaçıranlar S., Dawoud I.
openaire   +1 more source