Abstract
In this paper, the least squares estimator of random variables for a convex operator is investigated on LF∞(μ) space. We adopt much weaker conditions for the convex operator than in Ji et al. (2020) and Sun and Ji (2017). These weaker conditions can also guarantee that the minimax theorem holds. Due to Komlós theorem and the minimax theorem, the existence and uniqueness of the least squares estimator are obtained.
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