Robust fuzzy regression analysis

D’Urso, Pierpaolo; Massari, Riccardo; Santoro, Adriana

doi:10.1016/j.ins.2011.04.031

Cited by 77 publications

(32 citation statements)

References 53 publications

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“…Robust alternatives are being explored lastly (see, e.g., [28][29][30]). Concerning the location, in Sinova et al [30], a median for random fuzzy sets is defined.…”

Section: Robust Fuzzy Data Analysismentioning

confidence: 99%

“…One of the main issues that faces real data analysis is the effect that contamination produces in classical statistical measures or methods. This effect is directly inherited by the methods combining statistics and fuzziness and, for this reasons, some alternatives have been explored in particular problems (see, e.g., [28][29][30]). Within this context the aim of this paper is twofold:…”

Section: Introductionmentioning

confidence: 99%

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Fuzziness in data analysis: Towards accuracy and robustness

Colubi

González‐Rodríguez

2015

Fuzzy Sets and Systems

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“…Robust alternatives are being explored lastly (see, e.g., [28][29][30]). Concerning the location, in Sinova et al [30], a median for random fuzzy sets is defined.…”

Section: Robust Fuzzy Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzziness in data analysis: Towards accuracy and robustness

Colubi

González‐Rodríguez

2015

Fuzzy Sets and Systems

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“…In the literature, different statistical procedures for imprecise information are proposed (see, for example, Hung, 25 Sun and Wu, 31 Sinova et al 30 ). In the regression context in the last years the number of publications is grown (see, An et al, 1 Blanco-Fernández et al, 5 Cattaneo and Wiencierz, 8 D'Urso et al, 15 Giordani, 22 Körner and Näther 26 ). In this paper we restrict our attention to a family of regression models with imprecise information previously introduced: Ferraro et al 18,19 and Ferraro and Giordani.…”

Section: Introductionmentioning

confidence: 99%

On the Generalization Performance of a Regression Model with Imprecise Elements

Ferraro

2017

Int. J. Unc. Fuzz. Knowl. Based Syst.

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A linear regression model for imprecise random variables is considered. The imprecision of a random element has been formalized by means of the LR fuzzy random variable, characterized by a center, a left and a right spread. In order to avoid the non-negativity conditions the spreads are transformed by means of two invertible functions. To analyze the generalization performance of that model an appropriate prediction error is introduced, and it is estimated by means of a bootstrap procedure. Furthermore, since the choice of response transformations could affect the inferential procedures, a computational proposal is introduced for choosing from a family of parametric link functions, the Box-Cox family, the transformation parameters that minimize the prediction error of the model.

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“…By reducing the influence of outliers, the observation data are analyzed, calculated and summarized in a quantitative perspective [13]. Most of the recent work on spatial data has used various clustering techniques due to the nature of the data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Geographical Distribution of Missing Children Based on the Missing Children Mobile GIS Mutual Assistance System of China

Gong¹,

Cheng²,

Yang³

et al. 2017

GEP

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The Missing Children Mobile GIS Mutual Assistance System of China (MCMAS) is a mobile service software based on mobile GIS platform software, and it is committed to providing the most convenient and efficient system of personally mutual tracing services for missing children family and society. Relying on collaborative utilization of location-based service technology, face image intelligent recognition technology, cloud computing technology, public big data sharing technology, and mobile GIS technology, the MCMAS has achieved prominent application effects since it was deployed. At present, the MCMAS is running soundly, and it has received and released the information about 1011 missing children from May 25, 2016 to May 25, 2017. In order to explore the geographical distribution features and the influencing factors of missing children, the data of missing children are used for spatial and visual analysis by the data mining and GIS technologies. At the same time, we have built the spatial thermodynamic diagram of the big data of China missing children. By comparing provinces and cities with a higher proportion of missing children, the results showed that: 1) The high proportion of missing children spatially concentrated in the eastern part of the China. 2) The number of missing children was significantly correlated with the population density and economic status of the city. Furthermore, the paper macro-levelly presents a basic basis for rescuing the missing children from two aspects: regionally spatial characteristics and influencing factors.

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Robust fuzzy regression analysis

Cited by 77 publications

References 53 publications

Fuzziness in data analysis: Towards accuracy and robustness

Fuzziness in data analysis: Towards accuracy and robustness

On the Generalization Performance of a Regression Model with Imprecise Elements

Analysis of Geographical Distribution of Missing Children Based on the Missing Children Mobile GIS Mutual Assistance System of China

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