FLAT LIKELIHOODS: BINOMIAL CASE

José A. Montoya

doi:10.15446/rev.fac.cienc.v11n2.97888

FLAT LIKELIHOODS: BINOMIAL CASE

Translated title of the contribution: FLAT LIKELIHOODS: BINOMIAL CASE

José A. Montoya^*

^*Corresponding author for this work

Departamento de Matemáticas

Research output: Contribution to journal › Article › peer-review

Abstract

The shape of the likelihood function is often considered as one of the underlying causes of strange or counterintuitive estimation results. However, strange likelihood shapes may be a symptom of inferential issues related with the nature of the model and experimental data. In the cases discussed here, binomial flat likelihoods are related not only to sample size, but also to an embedded Poisson model problem. It is essential to understand the shapes of the likelihood function in order for being able to legitimately criticize likelihood inferences. This is particularly important since the likelihood function is a key ingredient in many inferential methods.

Translated title of the contribution	FLAT LIKELIHOODS: BINOMIAL CASE
Original language	English
Pages (from-to)	8-24
Number of pages	17
Journal	Revista de la Facultad de Ciencias
Volume	11
Issue number	2
DOIs	https://doi.org/10.15446/rev.fac.cienc.v11n2.97888
State	Published - Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 American Mathematical Society.

Keywords

Flat likelihood
Poisson distribution
embedded model
likelihood contours
profile likelihood function
threshold parameter

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10.15446/rev.fac.cienc.v11n2.97888

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AB - The shape of the likelihood function is often considered as one of the underlying causes of strange or counterintuitive estimation results. However, strange likelihood shapes may be a symptom of inferential issues related with the nature of the model and experimental data. In the cases discussed here, binomial flat likelihoods are related not only to sample size, but also to an embedded Poisson model problem. It is essential to understand the shapes of the likelihood function in order for being able to legitimately criticize likelihood inferences. This is particularly important since the likelihood function is a key ingredient in many inferential methods.

KW - Flat likelihood

KW - Poisson distribution

KW - embedded model

KW - likelihood contours

KW - profile likelihood function

KW - threshold parameter

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JF - Revista de la Facultad de Ciencias

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FLAT LIKELIHOODS: BINOMIAL CASE

Abstract

Bibliographical note

Keywords

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