Topic
Ordinal regression
About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.
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TL;DR: In this article, the efficacy of nonparametric procedures to improve the classification of the ejaculates in the artificial insemination ( AI) centers according to their fertility rank predicted from characteristics of the AI doses was evaluated.
7 citations
TL;DR: In this paper, the authors found that accurate placement within levels of an ESL program is crucial for optimal teaching and learning and that commercially available tests are commonly used for placement, but their effectiveness has not been found.
Abstract: Accurate placement within levels of an ESL program is crucial for optimal teaching and learning. Commercially available tests are commonly used for placement, but their effectiveness has been found...
7 citations
TL;DR: In this article, the authors present an approach which allows us to do a preliminary examination of the data set to see whether or not the statistical results are sensitive to the choice of procedure.
7 citations
TL;DR: In this article, the importance of the coding of effects in regularized categorical and ordinal regression was discussed and it was shown that, though an appropriate regularization is profitable for any coding, the choice of a relevant coding can prevail over the one of the regularization term for revealing structures.
Abstract: This discussion is a continuation of Tutz and Gertheiss (2016)’s paper, where we focus on the importance of the coding of effects in regularized categorical and ordinal regression. We show that, though that an appropriate regularization is profitable for any coding, the choice of a relevant coding can prevail over the one of the regularization term for revealing structures. We focus on predictors though the issues raised also apply to responses. We illustrate our point on a classic data set.
7 citations
01 Jan 2013
TL;DR: In this article, a new preference disaggregation method for multiple criteria sorting problems, called DIS-CARD, is presented, using the ordinal regression approach to construct a model of DM's preferences from preference information provided in terms of exemplary assignments of some reference alternatives, together with the above desired cardinalities.
Abstract: In this paper, we present a new preference disaggregation method for multiple criteria sorting problems, called DIS-CARD. Real-life experience indicates the need of con- sidering decision making situations in which a decision maker (DM) specifies a desired number of alternatives to be assigned to single classes or to unions of some classes. These situations require special methods for multiple criteria sorting subject to desired cardinalities of classes. DIS-CARD deals with such a problem, using the ordinal regression approach to construct a model of DM's preferences from preference information provided in terms of exemplary assignments of some reference alternatives, together with the above desired cardinalities. We develop a mathematical model for incorporating such preference informa- tion via mixed integer linear programming (MILP). Then, we adapt the MILP model to two types ofpreference models: an additive value function and an outranking relation. Illustrative example is solved to illustrate the methodology.
7 citations