Name
Abstract | ||
|---|---|---|
In this paper, we estimate the parameters of fuzzy regression models and investigate a statistical inferences with crisp inputs and fuzzy outputs for each $$\alpha $$ -cut. The proposed approaches of statistical inferences are fuzzy least squares (FLS) method and bootstrap technique. FLS is constructed on the basis of minimizing the sum of square of the total difference between observed and estimated outputs. Numerical examples are illustrated to perform the hypotheses test and to provide the percentile confidence regions by proposed approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s00500-014-1415-5 | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
Keywords | Field | DocType |
Fuzzy regression, Fuzzy least squares method, Bootstrap method | Least squares,Fuzzy logic,Fuzzy regression,Artificial intelligence,Statistical inference,Statistics,Bootstrapping (electronics),Mathematics,Machine learning,Percentile | Journal |
Volume | Issue | ISSN |
19 | 4 | 1433-7479 |
Citations | PageRank | References |
9 | 0.47 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Woo-Joo Lee | 1 | 14 | 2.94 |
| Hye Young Jung | 2 | 15 | 1.62 |
| Jin Hee Yoon | 3 | 77 | 10.77 |
| Seung-Hoe Choi | 4 | 73 | 8.89 |