Given data $X_1, \dots, X_n$ and a kernel h with m arguments, Serfling introduced the class of generalized L-statistics (GL-statistics), which is defined by taking linear combinations of the ordered
$h(X_{i_1}, \dots, X_{i_m})$ where $(i_1, \dots, i_m)$ ranges over all $n!/(n - m)!$ distinct m-tuples of $(1, \dots, n)$. In this paper we derive a class of incomplete generalized L-statistics (IGL-statistics) by taking linear combinations of the ordered elements from a subset of ${h(X_{i_1}, \dots, X_{i_m})}$ with size $N(n)$. A special case is the class of incomplete U-statistics, introduced by Blom. Under very general conditions, the IGL-statistic is asymptotically equivalent to the GL-statistic as soon as $N(n)/n \to \infty \as n \to \infty$, which makes the IGL much more computationally feasible. We also discuss various ways of selecting the subset of ${h(X_{i_1}, \dots, X_{i_m})}$. Several examples are discussed. In particular, some new estimates of the scale parameter in nonparametric regression are introduced. It is shown that these estimates are asymptotically equivalent to an IGL-statistic. Some extensions, for example, functionals other than L and multivariate kernels, are also addressed.