Manuel Montenegro
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Manuel Montenegro has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Ana Colubi
Universidad de Oviedo
2 shared publications - Gil González-Rodríguez
European Center for Soft Computing
2 shared publications - Ana Belén Ramos-Guajardo
University of Oviedo
1 shared publication - Angela Blanco-Fernández
University of Oviedo
1 shared publication - Beatriz Sinova
Department of Statistics and O.R. and D.M., University of Oviedo, Oviedo, Spain
1 shared publication Carlos Carleos
Universidad de Oviedo
1 shared publication
Latest Publications
- 1 March 2018
- Published by Springer Nature
- p. 905-912
Pedro Gil: Teacher, Researcher, Mentor, Leader and Colleague at the University of Oviedo
- 1 March 2018
- Published by Springer Nature
- p. 905-912
Pedro Gil: Teacher, Researcher, Mentor, Leader and Colleague at the University of Oviedo
- 1 March 2018
- Published by Springer Nature
- p. 709-719
A Case Study-Based Analysis of the Influence of the Fuzzy Data Shape in Quantifying Their Fréchet’s Variance
- 1 March 2018
- Published by Springer Nature
- p. 709-719
A Case Study-Based Analysis of the Influence of the Fuzzy Data Shape in Quantifying Their Fréchet’s Variance
- 1 January 2015
- Published by Springer Nature
- p. 143-150
The Wabl/Ldev/Rdev Median of a Random Fuzzy Number and Statistical Properties
- 1 January 2015
- Published by Springer Nature
- p. 143-150
The Wabl/Ldev/Rdev Median of a Random Fuzzy Number and Statistical Properties
- 1 January 2013
- Published by Springer Nature
- p. 105-113
A Linear Regression Model for Interval-Valued Response Based on Set Arithmetic
- 1 January 2013
- Published by Springer Nature
- p. 105-113
A Linear Regression Model for Interval-Valued Response Based on Set Arithmetic
- 1 January 2010
- Published by Springer Nature
- p. 537-544
Power Analysis of the Homoscedasticity Test for Random Fuzzy Sets
- 1 January 2010
- Published by Springer Nature
- p. 537-544
Power Analysis of the Homoscedasticity Test for Random Fuzzy Sets
- 1 June 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2007 IEEE International Fuzzy Systems Conference
- No. 10987584,p. 1-4
A determination coefficient for Fuzzy Random Variables in a fuzzy arithmetic-based linear model
A natural way of quantifying the degree of linear dependence between two fuzzy random variables in certain models is analyzed. In these models, the linear relationship is formalized in terms of a regression model based on the usual arithmetic for fuzzy sets. The degree of linear relationship is proposed to be measured by means of a kind of determination coefficient, that is, through the proportion of variability of the response fuzzy random variable explained by the regression model. Some properties of both the models and the determination coefficient are analyzed in order to verify the suitability of this coefficient as a measure of linear dependence. Finally, the meaning of a correlation coefficient which mimics the usual one for real-valued random variables is discussed.
- 1 June 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2007 IEEE International Fuzzy Systems Conference
- No. 10987584,p. 1-4
A determination coefficient for Fuzzy Random Variables in a fuzzy arithmetic-based linear model
A natural way of quantifying the degree of linear dependence between two fuzzy random variables in certain models is analyzed. In these models, the linear relationship is formalized in terms of a regression model based on the usual arithmetic for fuzzy sets. The degree of linear relationship is proposed to be measured by means of a kind of determination coefficient, that is, through the proportion of variability of the response fuzzy random variable explained by the regression model. Some properties of both the models and the determination coefficient are analyzed in order to verify the suitability of this coefficient as a measure of linear dependence. Finally, the meaning of a correlation coefficient which mimics the usual one for real-valued random variables is discussed.