Results 31 to 40 of about 2,450,039 (300)
Ordinal Ridge Regression with Categorical Predictors [PDF]
, 2011 In multi-category response models categories are often ordered. In case of ordinal response models, the usual likelihood approach becomes unstable with ill-conditioned predictor space or when the number of parameters to be estimated is large relative to ...
Zahid, Faisal Maqbool
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E-Jurnal Matematika, 2023 This research was conducted to determine the variables that have a significant impact on the stages of a well-off family in Sidemen Sub-district based on indicators obtained from the BKKBN and to classify the stages of a well-off family.
I GUSTI NGURAH SENTANA PUTRA +2 more
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Binary and Ordinal Random Effects Models Including Variable Selection [PDF]
, 2010 A likelihood-based boosting approach for fitting binary and ordinal mixed models is presented. In contrast to common procedures it can be used in high-dimensional settings where a large number of potentially influential explanatory variables is available.
Groll, Andreas, Tutz, Gerhard
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Selection of Ordinally Scaled Independent Variables [PDF]
, 2009 Ordinal categorial variables are a common case in regression modeling. Although the case of ordinal response variables has been well investigated, less work has been done concerning ordinal predictors.
Gertheiss, Jan +3 more
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Weighted k-Nearest-Neighbor Techniques and Ordinal Classification [PDF]
, 2004 In the field of statistical discrimination k-nearest neighbor classification is a well-known, easy and successful method. In this paper we present an extended version of this technique, where the distances of the nearest neighbors can be taken into ...
Schliep, Klaus +3 more
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Geographically Weighted Probit Ordinal Regression Model Estimation [PDF]
E3S Web of ConferencesGeographically Weighted Probit Ordinal Regression (GWPOR) is a combined method between Geographically Weighted Regression and Probit Ordinal Regression. This study estimates the percentage of poor people using the GWPOR method.
Kurniawan Muh. Idham +2 more
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Generating Correlated Ordinal Random Values [PDF]
, 2011 Ordinal variables appear in many field of statistical research. Since working with simulated data is an accepted technique to improve models or test results there is a need for providing correlated ordinal random values with certain properties like ...
Leisch, Friedrich +2 more
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Twitter Sentiment Analysis Based on Ordinal Regression
IEEE Access, 2019 In recent years, research on Twitter sentiment analysis, which analyzes Twitter data (tweets) to extract user sentiments about a topic, has grown rapidly. Many researchers prefer the use of machine learning algorithms for such analysis.
Shihab Elbagir Saad, Jing Yang
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Simultaneous optimization of quantitative and ordinal responses using Taguchi method [PDF]
International Journal of Research in Industrial Engineering, 2018 In the real world, the overall quality of a product is often represented partly by the measured values of some quantitative variables and partly by the observed values of some ordinal variables.
S. Pal, S. Gauri
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Evaluation Measures for Ordinal Regression
2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009 Ordinal regression (OR -- also known as ordinal classification) has received increasing attention in recent times, due to its importance in IR applications such as learning to rank and product review rating. However, research has not paid attention to the fact that typical applications of OR often involve datasets that are highly imbalanced.
Baccianella S, Esuli A, Sebastiani F
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