Results 261 to 270 of about 147,262 (309)

RiTex: Harmonization of Radiomic Features Based on Riemannian Geometry. [PDF]

J Imaging
Voitenko DA, Vladzymyrskyy AV, Omelyanskaya OV, Vasilev YA, Blokhin IA, Kodenko MR.   +5 more
europepmc   +1 more source

Longitudinal Relationship of eHealth Literacy With Self-Management Via Self-efficacy in Individuals With Type 2 Diabetes: Autoregressive Cross-Lagged Modeling. [PDF]

J Nurs Res
Lee EH, Lee KW, Kim HJ, Kang EH, Kang HJ.   +4 more
europepmc   +1 more source

Residual Asymmetry Modeling and Joint Time-Frequency Estimation for High-Dynamic Two-Way Microwave Links. [PDF]

Sensors (Basel)
Hao Z, Wu H.
europepmc   +1 more source

Reconstructing Spatial Localization Error Maps via Physics-Informed Tensor Completion for Passive Sensor Systems. [PDF]

Sensors (Basel)
Zhang Z, Huang Z, Wang C, Jiang Q.
europepmc   +1 more source

VQF-Based Decoupled Navigation Architecture for High-Curvature Maneuvering of Underwater Vehicles. [PDF]

Sensors (Basel)
Cui B, Lu Y, Zhang L, Chen F, Liang B, Yao P, Mu X, Yu S.   +7 more
europepmc   +1 more source

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Impact Evaluation Using Analysis of Covariance With Error-Prone Covariates That Violate Surrogacy

Evaluation Review, 2019
Background: Analysis of covariance (ANCOVA) is commonly used to adjust for potential confounders in observational studies of intervention effects. Measurement error in the covariates used in ANCOVA models can lead to inconsistent estimators of intervention effects.
J R Lockwood, Daniel F Mccaffrey
exaly   +3 more sources

DISEASE MAPPING WITH ERRORS IN COVARIATES

Statistics in Medicine, 1997
We describe Bayesian hierarchical-spatial models for disease mapping with imprecisely observed ecological covariates. We posit smoothing priors for both the disease submodel and the covariate submodel. We apply the models to an analysis of insulin Dependent Diabetes Mellitus incidence in Sardinia, with malaria prevalence as a covariate.
BERNARDINELLI, LUISA, PASCUTTTO C., BEST N. G., GILKS W. R.   +3 more
openaire   +2 more sources

Sensitivity of analysis error covariance to the mis‐specification of background error covariance

Quarterly Journal of the Royal Meteorological Society, 2012
AbstractMost data assimilation and satellite retrieval methods are based on optimal estimation theory, which assumes that the error covariances of the observations and of the a priori (background) information are known. The specification of background error covariance is crucial to the appropriate interpretation of radiance information from satellite ...
J. R. Eyre, F. I. Hilton
openaire   +1 more source

Analysis of Covariance with Non‐normal Errors

International Statistical Review, 2009
Summary Analysis of covariance techniques have been developed primarily for normally distributed errors. We give solutions when the errors have non‐normal distributions. We show that our solutions are efficient and robust. We provide a real‐life example.
Şenoğlu, Birdal, Avcıoğlu, Mubeccel Didem   +1 more
openaire   +2 more sources