Results 21 to 30 of about 263,732 (287)
Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology, 2013 We propose an extension of choquistic regression from the case of binary to the case of ordinal classification. Choquistic regression itself has been introduced recently as a generalization of conventional logistic regression. The basic idea of this method is to replace the linear function of predictor variables in the logistic regression model by the ...
Eyke Huellermeier, Ali Fallah Tehrani
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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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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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An Ordinal Regression Model using Dealer Satisfaction Data [PDF]
, 2007 This article analyses dealer satisfaction data in the agricultural technology market in Germany. The dealers could rate their suppliers in the 'overall satisfaction' and in 38 questions which can be summarized in 8 dimensions. An ordinal regression model
Alexander Staus
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Using rank data to estimate health state utility models [PDF]
, 2006 In this paper we report the estimation of conditional logistic regression models for the Health Utilities Index Mark 2 and the SF-6D, using ordinal preference data.
Aki Tsuchiya +24 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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Ordinal regression model for pea seed mass
Die Bodenkultur, 2017 The development of seeds at various positions in the pod is asynchronous. Thus, the differences of seed dry mass production because of environmental conditions may depend on the cultivar type, type of inoculants and interrelations between seeds per pod ...
Klimek-Kopyra Agnieszka +5 more
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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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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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Regression with Ordered Predictors via Ordinal Smoothing Splines
Frontiers in Applied Mathematics and Statistics, 2017 Many applied studies collect one or more ordered categorical predictors, which do not fit neatly within classic regression frameworks. In most cases, ordinal predictors are treated as either nominal (unordered) variables or metric (continuous) variables ...
Nathaniel E. Helwig, Nathaniel E. Helwig
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