Results 101 to 110 of about 1,354,279 (305)
Bayesian Prediction of Mean Square Errors with Covariates [PDF]
, 1992 Abstract : Estimation of mean square prediction error of wind components is required in the optimal interpolation (OI) process in numerical prediction of atmospheric variables. Previous work has suggested that statistical models with log-linear scale parameters which include covariates can be used to predict mean square prediction errors.
Gaver, Donald Paul, Jacobs, Patricia A.
openaire +2 more sources
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Purpose This study aims to develop a CycleGAN based denoising model to enhance the quality of low‐dose PET (LDPET) images, making them as close as possible to standard‐dose PET (SDPET) images. Methods Using a Philips Vereos PET/CT system, whole‐body PET images of fluorine‐18 fluorodeoxyglucose (18F‐FDG) were acquired from 37 patients to ...
Yang Liu, ZhiWu Sun, HaoJia Liu
wiley +1 more source
Modelling and forecasting volatility of East Asian Newly Industrialized Countries and Japan stock markets with non-linear models [PDF]
This paper explores the forecasting performances of several non-linear models, namely GARCH, EGARCH, APARCH used with three distributions, namely the Gaussian normal, the Student-t and Generalized Error Distribution (GED).
Guidi, Francesco
core +1 more source
Localization accuracy of 6‐second CBCT for lung IGRT with various breathing patterns
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Purpose The 6‐second cone beam computed tomography (CBCT) acquisition of the Ethos HyperSight (Varian Medical Systems, Inc. Palo Alto, CA, USA) on‐board imaging system offers benefits, but could be too fast to accurately capture an average target position in a free‐breathing lung cancer patient. This study aimed to ascertain whether a 6‐second
Jihye Koo +5 more
wiley +1 more source
پژوهشهای ریاضی, 2018 Introduction According to the classic sampling theory, errors that are mainly considered in the estimations are sampling errors. However, most non-sampling errors are more effective than sampling errors in properties of estimators.
Leader Navaei, Rasoul Imaz
doaj
Window Length Selection and Signal-Noise Separation and Reconstruction in Singular Spectrum Analysis [PDF]
In Singular Spectrum Analysis (SSA) window length is a critical tuning parameter that must be assigned by the practitioner. This paper provides a theoretical analysis of signal-noise separation and reconstruction in SSA that can serve as a guide to ...
D.S. Poskitt, Md Atikur Rahman Khan
core
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Purpose Histotripsy is a focal tumor therapy that utilizes focused ultrasound (US) to mechanically destroy tissue. To overcome visualization limitations of diagnostic US‐guidance, C‐arm cone beam CT (CBCT)‐guided histotripsy is being developed, for which a mobile C‐arm could increase accessibility. CBCT‐guided histotripsy uses a phantom with a
Grace M. Minesinger +5 more
wiley +1 more source
Bootstrap for estimating the mean squared error of the spatial EBLUP [PDF]
This work assumes that the small area quantities of interest follow a Fay-Herriot model with spatially correlated random area effects. Under this model, parametric and nonparametric bootstrap procedures are proposed for estimating the mean squared error ...
Isabel Molina +2 more
core
Root mean square error (RMSE) or mean absolute error (MAE)? [PDF]
, 2014 Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that ...
T. Chai, R. R. Draxler
openaire +2 more sources
Precision‐Optimised Post‐Stroke Prognoses
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Current medicine cannot confidently predict who will recover from post‐stroke impairments. Researchers have sought to bridge this gap by treating the post‐stroke prognostic problem as a machine learning problem, reporting prediction error metrics across samples of patients whose outcomes are known.
Thomas M. H. Hope +4 more
wiley +1 more source