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August 13, 2026Statistics

Asymptotic properties of estimators in heteroscedastic partially linear EV models with ANA errors

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Overview

This randomized trial evaluates estimators in heteroscedastic EV models, exploring their consistency properties and performance implications.

Key Points

  • The study aims to establish the consistency properties of estimators in heteroscedastic partially linear error-in-variables models under ANA errors.
  • Developed estimators for slope parameter and nonparametric component in known and unknown error variance scenarios.
  • Performed simulations to assess the performance of these estimators in finite samples.
  • Examined strong consistency properties for estimators based on ANA samples.
  • Establish strong consistency for slope parameter and nonparametric component under known error variance.
  • Show that estimators maintain strong consistency even when the error variance is unknown.
  • Simulation results support the theoretical findings regarding the estimators' performance.

Cite This Study

A 2026 study studied this question.

synapsesocial.com/papers/6a7d75d82b0e0cff3f63ebf7https://doi.org/10.1080/02331888.2026.2707316
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Asymptotic properties of wavelet estimators in a semi-parametric EV models with ANA errors2026
  2. 2Mean Consistency of Estimators in a Partially Linear Model with AANA Errors2026
  3. 3Asymptotic normality for the estimators in heteroscedastic semiparametric EV models with α-mixing errors2024
  4. 4The rates of strong consistency for estimators in heteroscedastic partially linear errors-in-variables model for widely orthant dependent samples2024 · 1 citations
  5. 5Asymptotic Properties of Conditional Value-at-risk Estimate for Asymptotic Negatively Associated Samples2024