























Abstract:We consider statistical inference for a class of continuous semimartingale regression models based on high-frequency observations subject to contamination by finite-activity jumps and spike noise. By employing density-power weighting and Hölder-inequality-based normalization, we propose easy-to-implement, robustified versions of the conventional Gaussian quasi-maximum-likelihood estimator that require only a single tuning parameter. We prove their asymptotic mixed normality at the standard rate of $\sqrt{n}$. It is theoretically shown that these estimators are simultaneously robust against contamination in both the covariate and response processes. Additionally, under suitable conditions on the selection of the tuning parameter, the proposed estimators achieve the same asymptotic distribution as the conventional estimator in the contamination-free case. Illustrative simulation results highlight the estimators' insensitivity to the choice of the tuning parameter.
From: Hiroki Masuda [view email]
[v1]
Fri, 3 Oct 2025 01:49:35 UTC (1,081 KB)
[v2]
Fri, 24 Oct 2025 03:40:01 UTC (1,099 KB)
[v3]
Wed, 31 Dec 2025 13:34:08 UTC (655 KB)
[v4]
Sat, 13 Jun 2026 12:07:01 UTC (804 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。