Name
Abstract | ||
|---|---|---|
This paper applies a ridge estimation approach in an existing partial logistic regression model with exact predictors, intuitionistic fuzzy responses, intuitionistic fuzzy coefficients and intuitionistic fuzzy smooth function to improve an existing intuitionistic fuzzy partial logistic regression model in the presence of multicollinearity. For this purpose, ridge methodology is also involved to estimate the parametric intuitionistic fuzzy coefficients and nonparametric intuitionistic fuzzy smooth function. Some common goodness-of-fit criteria are also used to examine the performance of the proposed regression model. The potential application of the proposed method are illustrated and compared with the intuitionistic partial logistic regression model through two numerical examples. The results clearly indicate the proposed ridge method is quite efficient in model's performances when there is multicollinearity among the predictors. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1142/S0218488520500221 | INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS |
Keywords | DocType | Volume |
Goodness-of-fit measure, intuitionistic fuzzy number, intuitionistic fuzzy partial logistic regression, kernel method, ridge estimation | Journal | 28 |
Issue | ISSN | Citations |
4 | 0218-4885 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gholamreza Hesamian | 1 | 69 | 15.53 |
| Mohammad Ghasem Akbari | 2 | 31 | 12.04 |
| Mehdi Roozbeh | 3 | 0 | 0.34 |