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The least squares estimator of random variables under convex operators on LF∞(μ) space

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  • Sun, Chuanfeng
  • Ji, Shaolin
  • Kong, Chuiliu

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.

Suggested Citation

  • Sun, Chuanfeng & Ji, Shaolin & Kong, Chuiliu, 2022. "The least squares estimator of random variables under convex operators on LF∞(μ) space," Statistics & Probability Letters, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:stapro:v:181:y:2022:i:c:s0167715221002303
    DOI: 10.1016/j.spl.2021.109268
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    References listed on IDEAS

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