Results 41 to 50 of about 109,915 (302)
Implementing Analysis of Ordinal Regression Model on Student’s Feedback Response
Cihan University-Erbil Journal of Humanities and Social Sciences, 2021 Instruction is a multidimensional procedure including a quantity of features, e.g., tutor qualities, that occasionally are hard to assess. In certain points, education efficiency, that is a part of instructing, is affected by a combination of teacher ...
Dler H. Kadir, Ameera W. Omer
doaj +1 more source
Robust Estimation and Filtering Methods for Ordinal Label Noise [PDF]
Jisuanji kexueLarge-scale labeled datasets inevitably contain label noise,which limits the generalization performance of the model to some extent.The labels of ordinal regression datasets are discrete values,but there exist ordinal relationships between different ...
JIANG Gaoxia, WANG Fei, XU Hang, WANG Wenjian
doaj +1 more source
Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution
EURASIP Journal on Advances in Signal Processing, 2007 We present a supervised learning-based approach for subpixel motion estimation which is then used to perform video super-resolution. The novelty of this work is the formulation of the problem of subpixel motion estimation in a ranking framework.
Nemanja Petrovic +3 more
doaj +2 more sources
Modeling migraine severity with autoregressive ordered probit models [PDF]
, 2005 This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics. Since ordinal severity measurements arise from a single patient dependencies among the measurements have to be accounted ...
Heyn, Anette +3 more
core +1 more source
mvord: An R Package for Fitting Multivariate Ordinal Regression Models
Journal of Statistical Software, 2020 The R package mvord implements composite likelihood estimation in the class of multivariate ordinal regression models with a multivariate probit and a multivariate logit link.
Rainer Hirk, Kurt Hornik, Laura Vana
doaj +1 more source
Regularized Proportional Odds Models [PDF]
, 2011 The proportional odds model is commonly used in regression analysis to predict the outcome for an ordinal response variable. The maximum likelihood approach becomes unstable or even fails in small samples with relatively large number of predictors.
Heumann, Christian +1 more
core +1 more source
A constrained regression model for an ordinal response with ordinal predictors [PDF]
Statistics and Computing, 2018 A regression model is proposed for the analysis of an ordinal response variable depending on a set of multiple covariates containing ordinal and potentially other variables. The proportional odds model (McCullagh (1980)) is used for the ordinal response, and constrained maximum likelihood estimation is used to account for the ordinality of covariates ...
Espinosa, Javier, Hennig, Christian
openaire +5 more sources
Ordinal regression of adherence level.
, 2021 Ordinal regression of adherence level.
Raghda Qarqaz (10345718) +4 more
core +1 more source
Molecular Oncology, EarlyView.Many patients with urothelial cancer do not benefit from treatment with pembrolizumab, while at risk of severe side effects. Changes in the levels of circulating tumor DNA early during treatment, measured by a simple and affordable assay that can be easily implemented in the clinic, can be used as a prognostic tool to identify these patients.
Youssra Salhi +14 more
wiley +1 more source
Impact of Asymptomatic Intracranial Hemorrhage on Outcome After Endovascular Stroke Treatment
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Endovascular treatment (EVT) achieves high rates of recanalization in acute large‐vessel occlusion (LVO) stroke, but functional recovery remains heterogeneous. While symptomatic intracranial hemorrhage (sICH) has been well studied, the prognostic impact of asymptomatic intracranial hemorrhage (aICH) after EVT is less certain ...
Shihai Yang +22 more
wiley +1 more source