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the claim
Quantile regression estimators are statistically consistent under regularity conditions.
the verdict
SUPPORTED
the evidence backs this
refutedsupported
the weight of evidence
5 sources for · 0 against

Multiple recent statistical studies formally establish that quantile regression estimators possess statistical consistency and asymptotic normality under appropriate regularity conditions.

Evidence for · 5
2026 · cited by 0
Establishes strong consistency with explicit convergence rates for kernel quantile regression estimators under general regularity conditions.
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The analysis

The retrieved papers consistently support the claim that quantile regression estimators achieve statistical consistency under regularity conditions. Papers [2], [3], [4], [5], and [9] explicitly establish the theoretical consistency and asymptotic properties of various quantile regression estimators (such as high-dimensional, tensor, stratified, cure model, and additive variants) under standard regularity assumptions. None of the papers refute the claim. Therefore, the balance verdict is SUPPORTED.

More for · 4
2026 · cited by 0
Establishes statistical properties, including consistency of estimators for regularized tensor quantile regression.
2026 · cited by 0
Proves consistency and asymptotic normality for estimators in stratified quantile regression models.
2026 · cited by 0
Establishes asymptotic properties including consistency of estimators in a quantile cure model framework.
2026 · cited by 0
Establishes asymptotic properties and consistency for partially linear additive quantile regression estimators under censoring.
The paper trail · every fact has a biography
first checked04 Aug 2026
judged → SUPPORTED · 7504 Aug 2026
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