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The Influence of Prior Distributions in Modeling Conway-Maxwell-Poisson Data

Abstract Number:

3113 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Mark Meyer (1), Amia Graye (2), Kimberly Sellers (3)

Institutions:

(1) Georgetown University, N/A, (2) Office of Biostatistics Research, NHLBI, N/A, (3) North Carolina State University, N/A

Co-Author(s):

Amia Graye  
Office of Biostatistics Research, NHLBI
Kimberly Sellers  
North Carolina State University

First Author:

Mark Meyer  
Georgetown University

Presenting Author:

Kimberly Sellers  
North Carolina State University

Abstract Text:

Bayesian studies to date involving the Conway-Maxwell-Poisson (COM-Poisson) distribution have little discussion of non- and weakly-informative priors. This work considers various priors and evaluates their influence on the COM-Poisson model via an empirical study under varying dispersion types and sample sizes.

Keywords:

dispersion|over-dispersion|under-dispersion|Bayesian methodology|count data|

Sponsors:

Section on Bayesian Statistical Science

Tracks:

Bayesian Theory and Foundations

Can this be considered for alternate subtype?

No

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I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

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