Robust regression and outlier detection
Robust regression and outlier detection
Information Sciences: an International Journal
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Fuzzy Sets and Systems
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Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
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Fuzzy Sets and Systems
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Fuzzy Sets and Systems
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Fuzzy Sets and Systems
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Fuzzy Sets and Systems
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Fuzzy Sets and Systems
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Information Sciences: an International Journal
Information Sciences: an International Journal
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Computational Statistics & Data Analysis
Information Sciences: an International Journal
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Information Sciences: an International Journal
Outlier detection and least trimmed squares approximation using semi-definite programming
Computational Statistics & Data Analysis
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Information Sciences: an International Journal
Information Sciences: an International Journal
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Applied Soft Computing
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In this paper we propose a robust fuzzy linear regression model based on the Least Median Squares-Weighted Least Squares (LMS-WLS) estimation procedure. The proposed model is general enough to deal with data contaminated by outliers due to measurement errors or extracted from highly skewed or heavy tailed distributions. We also define suitable goodness of fit indices useful to evaluate the performances of the proposed model. The effectiveness of our model in reducing the outliers influence is shown by using applicative examples, based both on simulated and real data, and by a simulation study.