Quantile regression estimators are statistically consistent under regularity conditions.

the verdict

SUPPORTED

the evidence backs this

Recorded sources

5 sources for · 0 against

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Is it true that Quantile regression estimators are statistically consistent under regularity conditions?

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

Verdict: SUPPORTED · 5 sources for, 0 against · Last checked 4 Aug 2026

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.

Evidence for · 5

Uniform in Bandwidth Consistency of the <i>L</i><sup>1</sup>-Modal Regression Estimator for High-Dimensional Data.

2026

Establishes strong consistency with explicit convergence rates for kernel quantile regression estimators under general regularity conditions.

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FA, A., MB, A., & A, L. (2026). Uniform in Bandwidth Consistency of the <i>L</i><sup>1</sup>-Modal Regression Estimator for High-Dimensional Data.. doi.org. https://doi.org/10.3390/e28050558

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More for · 4

Regularized Tensor Quantile Regression With Applications to Neuroimaging Data Analysis.

2026

Establishes statistical properties, including consistency of estimators for regularized tensor quantile regression.

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M, P., D, Y., I, M., B, J., & L, K. (2026). Regularized Tensor Quantile Regression With Applications to Neuroimaging Data Analysis.. doi.org. https://doi.org/10.1002/sim.70582

Estimating Conditional Complier Quantile Treatment Effect via Stratified Quantile Regression.

2026

Proves consistency and asymptotic normality for estimators in stratified quantile regression models.

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H, M., M, P., & J, Q. (2026). Estimating Conditional Complier Quantile Treatment Effect via Stratified Quantile Regression.. doi.org. https://doi.org/10.1002/sim.70470

A quantile cure model with partially functional covariate effects.

2026

Establishes asymptotic properties including consistency of estimators in a quantile cure model framework.

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CM, C. & Y, P. (2026). A quantile cure model with partially functional covariate effects.. doi.org. https://doi.org/10.1177/09622802261445414

Partially Linear Additive Quantile Regression: Theory and Applications to Breast Cancer Patients' Survival.

2026

Establishes asymptotic properties and consistency for partially linear additive quantile regression estimators under censoring.

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X, Z. & M, T. (2026). Partially Linear Additive Quantile Regression: Theory and Applications to Breast Cancer Patients' Survival.. doi.org. https://doi.org/10.1002/sim.70463

Receipt History

first checked04 Aug 2026

judged → SUPPORTED · 7504 Aug 2026

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