Results 11 to 20 of about 96,202 (258)
Distributed Support Vector Ordinal Regression over Networks [PDF]
Entropy, 2022 Ordinal regression methods are widely used to predict the ordered labels of data, among which support vector ordinal regression (SVOR) methods are popular because of their good generalization.
Huan Liu, Jiankai Tu, Chunguang Li
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Regularized Ordinal Regression and the ordinalNet R Package [PDF]
Journal of Statistical Software, 2021 Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection.
Michael J. Wurm +2 more
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Ordinal regression increases statistical power to predict epilepsy surgical outcomes [PDF]
Epilepsia Open, 2022 Studies of epilepsy surgery outcomes are often small and thus underpowered to reach statistically valid conclusions. We hypothesized that ordinal logistic regression would have greater statistical power than binary logistic regression when analyzing ...
Adam S. Dickey +2 more
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Predicting progression of Alzheimer's disease using ordinal regression. [PDF]
PLoS ONE, 2014 We propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD ...
Orla M Doyle +10 more
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ordinalgmifs: An R Package for Ordinal Regression in High-dimensional Data Settings [PDF]
Cancer Informatics, 2014 High-throughput genomic assays are performed using tissue samples with the goal of classifying the samples as normal < pre-malignant < malignant or by stage of cancer using a small set of molecular features.
Kellie J. Archer +5 more
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Distributed Ordinal Regression Over Networks
IEEE Access, 2021 Many real-world data are labeled with natural orders, i.e., ordinal labels. Examples can be found in a wide variety of fields. Ordinal regression is a problem to predict ordinal labels for given patterns.
Huan Liu, Jiankai Tu, Chunguang Li
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ORDINAL LOGISTIC REGRESSION MODEL AND CLASSIFICATION TREE ON ORDINAL RESPONSE DATA
Barekeng, 2022 Logistic regression (LR) is a model that associates the relationship between category-type response variables with quantitative or quantitative and qualitative predictor variables. The prediction of the LR model is in the form of probability.
Jajang Jajang +2 more
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Statistical Analysis of Ordinal Response Variable: A Comparative Study [PDF]
المجلة العراقية للعلوم الاحصائية, 2022 Response variables in biological phenomena vary between three types: numerical response variables, ordinal categorical response variables, and nominal categorical response variables. In statistical studies, handling ordinal variables varies in accordance
Liqaa Alhamdany, Zaid Tariq Salah
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Ordinal Regression Model of Parking Search Time
Promet (Zagreb), 2023 Parking search reduces the quality of parking service, as well as traffic network level of service, due to additionally generated traffic. Parking search also entails other negative effects, primarily ecological, social and economic.
Jelena Simićević +2 more
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Jambura Journal of Mathematics, 2023 Diabetes is one of the high-risk diseases. The most prominent symptom of this disease is high blood sugar levels. People with diabetes in Indonesia can reach 30 million people.
Assyifa Lala Pratiwi Hamid +4 more
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