Assume that $f ({\bf x}; \theta): \mathbb{R}^p \times \Theta \to \mathbb{R}$, where ${\bf x}$ is the vector of inputs (with some distribution) and $\theta$ is the vector of parameters.
Also, assume that $E_{\bf x}[f({\bf x};\theta)]$ is maximized at $\theta^*$.
To estimate $\theta^*$ I solve the following problem: $$ \widehat{\theta} = \arg\max_{\theta} \frac{1}{n} \sum_{i=1}^n f ({\bf x}_i; \theta), \qquad (1) $$ where ${\bf x}_i$ are i.i.d. samples.
If $f$ is differentiable, under some regularity conditions, it is very well-known that $\widehat{\theta}$ is a consistent estimator of $\theta^*$, and is also asymptotically Normal.
In my research I have encountered a problem where $f$ is discontinuous and non-differentiable (actually it is piece-wise constant), but its expected value $E_{\bf x}[f({\bf x};\theta)]$ is continuous and differentiable.
I have shown that solving (1) gives a consistent estimator for $\theta^*$, but cannot prove the asymptotic normality of the estimator.
To me this seems like a general problem which must have been addressed in the literature, but cannot find any reference.
Is there a paper that has addressed this problem?