Home > Open Access Journals > JMASM > Vol. 7 (2008) > Iss. 2

#### Abstract

Consider the regression model *Y* = *γ*(*X*) + *ε* , where γ(*X*) is some conditional measure of location associated with *Y* , given *X*. Let *Υ̂* be some estimate of *Y*, given *X*, and let τ^{2} (*Y*) be some measure of variation. Explanatory power is η^{2} = τ^{2} (*Υ̂*) /τ^{2}(*Y*) . When *γ*(*X*) = *β_{0} + β_{1}X and τ^{2}(Y) is the variance of Y , η^{2} = ρ^{2} , where ρ is Pearson's correlation. The small-sample properties of some methods for estimating a robust analog of explanatory power via smoothers is investigated. The robust version of a smoother proposed by Cleveland is found to be best in most cases.*