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September 1, 1979The Annals of Statistics118 citationsOpen Access

Asymptotic Properties of Maximum Likelihood Estimators Based on Conditional Specification

PSPranab Kumar SenTata Institute of Fundamental Research

Key Points

  • The research aims to derive and compare the asymptotic properties of preliminary test maximum likelihood estimators and their unrestricted counterparts.
  • Derived asymptotic distribution, bias, and dispersion matrix for preliminary test maximum likelihood estimator.
  • Compared these properties with unrestricted and restricted maximum likelihood estimators.
  • Analyzed performance under scenarios where null hypothesis constraints may not hold.
  • Established asymptotic properties indicating robustness of preliminary test maximum likelihood estimator.
  • Showed comparison values of bias and dispersion between estimator types.
  • Highlighted that robustness holds even when assumptions about parameters are violated.

Abstract

Along with the asymptotic distribution, expressions for the asymptotic bias and asymptotic dispersion matrix of the preliminary test maximum likelihood estimator for a general multi-sample parametric model (when the null hypothesis relating to the restraints on the parameters may not hold) are derived and compared with the parallel expressions for the unrestricted and restricted maximum likelihood estimators. This study reveals the robustness property of the preliminary test estimator when the assumed restraints may not hold.

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Cite This Study

Pranab Kumar Sen (1979) studied this question.

synapsesocial.com/papers/6a1562d6a2f71238514e5f1ehttps://doi.org/10.1214/aos/1176344785
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Also Consider

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

  1. 1Maximum Likelihood Estimation of Misspecified Models1983 · 3,762 citations
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  5. 5A simple specification test for models with many conditional moment inequalities2024 · 1 citations