  We present the asymptotic distribution theory for a class of increment-based
estimators of the fractal dimension of a random field of the form g{X(t)},
where g:R\to R is an unknown smooth function and X(t) is a real-valued
stationary Gaussian field on R^d, d=1 or 2, whose covariance function obeys a
power law at the origin. The relevant theoretical framework here is ``fixed
domain'' (or ``infill'') asymptotics. Surprisingly, the limit theory in this
non-Gaussian case is somewhat richer than in the Gaussian case (the latter is
recovered when g is affine), in part because estimators of the type considered
may have an asymptotic variance which is random in the limit. Broadly, when g
is smooth and nonaffine, three types of limit distributions can arise, types
(i), (ii) and (iii), say. Each type can be represented as a random integral.
More specifically, type (i) can be represented as the integral of a certain
random function with respect to Lebesgue measure; type (ii) can be represented
as the integral of a second random function
