This paper considers the problem of estimating probabilities of the form P(Y ≤ w), for a given value of w, in the situation that a sample of i.i.d. observations X 1 , . . . , X n of X is available, and where we explicitly know a functional relation between the Laplace transforms of the non-negative random variables X and Y . A plug-in estimator is constructed by calculating the Laplace transform of the empirical distribution of the sample X 1 , . . . , X n , applying the functional relation to it, and then (if possible) inverting the resulting Laplace transform and evaluating it in w. We show, under mild regularity conditions, that the resulting estimator is weakly consistent and has expected absolute estimation error O(n −1/2 log(n+1)). We illustrate our results by two examples: in the first we estimate the distribution of the workload in an M/G/1 queue from observations of the input in fixed time intervals, and in the second we identify the distribution of the increments when observing a compound Poisson process at equidistant points in time (usually referred to as "decompounding").

In this note we study the behavior of maximum quasilikelihood estimators (MQLEs) for a class of statistical models, in which only knowledge about the first two moments of the response variable is assumed. This class includes, but is not restricted to, generalized linear models with general link function. Our main results are related to guarantees on existence, strong consistency and mean square convergence rates of MQLEs. The rates are obtained from first principles and are stronger than known a.s. rates. Our results find important application in sequential decision problems with parametric uncertainty arising in dynamic pricing.