We introduce a new fuzzy linear regression method. The method is capable of approximating relationships between an independent and dependent variable. variables are expected to be real value triangular numbers, respectively. demonstrate on twenty datasets that the reliable, it less sensitive outliers, compare with possibilistic-based methods. Unlike other commonly used methods, presented simple...

We apply a general algorithm for merging prediction strategies (the Aggregating Algorithm) to the problem of linear regression with the square loss; our main assumption is that the response variable is bounded. It turns out that for this particular problem the Aggregating Algorithm resembles, but is slightly different from, the wellknown ridge estimation procedure. From general results about th...

We consider the on-line predictive version of the standard problem of linear regression; the goal is to predict each consecutive response given the corresponding explanatory variables and all the previous observations. The standard treatment of prediction in linear regression analysis has two drawbacks: (1) the usual prediction intervals guarantee that the probability of error is equal to the n...