The least squares estimator of random variables under sublinear expectations

In this paper, we study the least squares estimator for sublinear expectations. Under some mild assumptions, we prove the existence and uniqueness of the least squares estimator. The relationship between the least squares estimator and the conditional coherent risk measure (resp. the conditional g-expectation) is also explored. Then some characterizations of the least squares estimator are given.