ENTENDIENDO UN PROBLEMA DE VEROSIMILITUD PLANA: INFERENCIAS SOBRE EL COCIENTE DE COEFICIENTES DE REGRESION EN MODELOS LINEALES

Translated title of the contribution: UNDERSTANDING A LIKELIHOOD FLAT PROBLEM: INFERENCES ON THE RATIO OF REGRESSION COEFFICIENTS IN LINEAR MODELS

Jorge Espíndola Zepeda, José A. Montoya*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we analyze a flat likelihood function shape that arises when performing inferences on the ratio of two regression coefficients in a linear regression model, parameter of interest in various applications. Due to this shape, infinite length likelihood-confidence intervals can be obtained. In the cases discussed here these likelihood- confidence intervals are related to the nested models problem, which is analyzed in detail through three illustrative simulated cases. It is essential to understand the shapes of the likelihood function in order to legitimately criticize likelihood inferences. This is of particular importance since the likelihood function is a key ingredient used in many inference methods.

Translated title of the contributionUNDERSTANDING A LIKELIHOOD FLAT PROBLEM: INFERENCES ON THE RATIO OF REGRESSION COEFFICIENTS IN LINEAR MODELS
Original languageSpanish
Pages (from-to)25-38
Number of pages14
JournalRevista de la Facultad de Ciencias
Volume11
Issue number2
DOIs
StatePublished - Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 American Mathematical Society.

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