Asymptotic properties of the multivariate Sz\'{a}sz-Mirakyan estimator for cumulative distribution functions on the nonnegative orthant
Guanjie Lyu, Fr\'ed\'eric Ouimet, Cindy Feng

TL;DR
This paper develops a comprehensive asymptotic theory for multivariate Szász-Mirakyan estimators of cdfs on the nonnegative orthant, highlighting differences between interior and boundary behaviors and demonstrating efficiency gains from Poisson smoothing.
Contribution
It provides explicit bias and variance expansions, establishes asymptotic efficiency, and analyzes boundary effects, advancing understanding of multivariate cdf estimation with Szász-Mirakyan estimators.
Findings
Poisson smoothing reduces variance in the interior, improving efficiency.
Boundary behavior differs; smoothing benefits diminish near the boundary.
Central limit theorems and uniform consistency are proved.
Abstract
The asymptotic properties of multivariate Sz\'{a}sz-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 , bias and variance expansions are obtained that differ fundamentally from those in the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStatistical Methods and Inference · Financial Risk and Volatility Modeling · Bayesian Methods and Mixture Models
