Abstract
Since, in statistics, it is a key task to pick the best out of a set of models to describe a given data set, the verification of this choice should be done with certain care. Commonly, model selection is done based on an information criterion, followed by subsequent checks of model adequacy. In this paper, further, more specific criteria for counts are proposed to validate the selected model. The procedure is exemplified by a count data application.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Falls, L.W., Williford, W.O., Carter, M.C.: Probability distributions for thunderstorm activity at Cape Kennedy, Florida. J. Appl. Meteorol. 10(1), 97–104 (1971)
Fisher, R.A.: The significance of deviations from expectation in a Poisson series. Biometrics 6, 17–24 (1950)
Göb, R.: Estimating value at risk and conditional value at risk for count variables. Qual. Reliab. Eng. Int. 27, 659–672 (2011)
Homburg, A.: Criteria for evaluating approximations to count distributions. Commun. Stat.: Simul. Comput. forthcoming (2018)
Möller, T., Weiß, C.H., Kim, H.-Y.: Modeling counts with state-dependent zero inflation. Stat. Model. forthcoming (2018)
Van den Broek, J.: A score test for zero inflation in a Poisson distribution. Biometrics 51(2), 738–743 (1995)
Weiß, C.H., Homburg, A., Puig, P.: Testing for zero inflation and overdispersion in INAR(1) models. Stat. Pap. forthcoming (2016)
Acknowledgements
The author is grateful to the referee and to Prof. Dr. Christian H. Weiß (Helmut Schmidt University) for their useful comments, which greatly improved this article.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Homburg, A. (2019). Criteria to Validate Count Data Model Selection. In: Steland, A., Rafajłowicz, E., Okhrin, O. (eds) Stochastic Models, Statistics and Their Applications. SMSA 2019. Springer Proceedings in Mathematics & Statistics, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-28665-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-28665-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28664-4
Online ISBN: 978-3-030-28665-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)