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What is Learnable in Valiant's Theory of the Learnable? 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Asymptotic properties of the multivariate Szász-Mirakyan estimator for cumulative distribution functions on the nonnegative orthant
Guanjie Lyu, Frédéric Ouimet, Cindy Feng · 2026-01-26 · via math.ST updates on arXiv.org

The asymptotic properties of multivariate Szász-Mirakyan estimators for cumulative distribution functions (cdf) supported on the nonnegative orthant are investigated. Explicit bias and variance expansions are derived on compact subsets of the interior, yielding sharp mean squared error characterizations and optimal smoothing rates. The analysis shows that the proposed Poisson smoothing yields a non-negligible variance reduction relative to the empirical cdf, leading to asymptotic efficiency gains that can be quantified through local and global deficiency measures. The behavior of the estimator near the boundary of its support is examined separately. Under a boundary-layer scaling that preserves nondegenerate Poisson smoothing as the evaluation point approaches the boundary of $[0,\infty)^d$, bias and variance expansions are obtained that differ fundamentally from those in the interior region. In particular, the variance reduction mechanism disappears at leading order, implying that no asymptotically optimal smoothing parameter exists in the boundary regime. Central limit theorems and almost sure uniform consistency are also established. Together, these results provide a unified asymptotic theory for multivariate Szász-Mirakyan cdf estimation and clarify the distinct roles of smoothing in the interior and boundary regions.

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