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Abstract 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… Click to show full abstract
Abstract 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.
Keywords:
squares estimator;
random variables;
estimator random;
least squares;
variables sublinear;
sublinear expectations
Journal Title: Journal of Mathematical Analysis and Applications
Year Published: 2017
Link to full text (if available)
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Journal Title: Journal of Mathematical Analysis and Applications
Year Published: 2017
Link to full text (if available)
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recommendations!