Each week, we select a recently published Open Access article to feature. This week’s article comes from the Scandinavian Journal of Statistics and examines the conditional Monte Carlo method.
The article’s abstract is given below, with the full article available to read here.
2021). Conditional Monte Carlo revisited. Scand J Statist, 1– 26. https://doi.org/10.1111/sjos.12549, , & (
Conditional Monte Carlo refers to sampling from the conditional distribution of a random vectorgiven the value for a function . Classical conditional Monte Carlo methods were designed for estimating conditional expectations of functions by sampling from unconditional distributions obtained by certain weighting schemes. The basic ingredients were the use of importance sampling and change of variables. In the present paper we reformulate the problem by introducing an artificial parametric model in which is a pivotal quantity, and next representing the conditional distribution of given within this new model. The approach is illustrated by several examples, including a short simulation study and an application to goodness-of-fit testing of real data. The connection to a related approach based on sufficient statistics is briefly discussed.More Details