Improved point estimation for inverse gamma regression models

This paper develops a bias correction scheme for reparametrized inverse gamma regression models with varying precision [Bourguignon M, Gallardo DI. Reparametrized inverse gamma regression models with varying precision. Stat Neerl. 2020;74(4):611–627], which is tailored to situations where the response variable has an asymmetrical shape on the positive real line. In particular, we discuss maximum-likelihood estimation for the model parameters and derive closed-form expressions for the first-order bias of the estimators. The expressions derived are simple and only require the definition of a few matrices. This enables us to obtain corrected estimators that are approximately unbiased. We conduct an extensive Monte Carlo simulation study to evaluate the performance of the proposed corrected estimators. Finally, we apply the results obtained in three real-world datasets. This paper contains Supplementary Material.

• Publication year

  2021

• Venue

  Journal of Statistical Computation and Simulation

• Publication date

  2021-03-12

• Fields of study

  Mathematics

• Identifiers
  DOI 10.1080/00949655.2021.1898611
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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