IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v144y2020ics0167947319302336.html
   My bibliography  Save this article

Some goodness-of-fit tests for the Poisson distribution with applications in Biodosimetry

Author

Listed:
  • Puig, Pedro
  • Weiß, Christian H.

Abstract

New characterizations of the Poisson distribution based on an identity involving the Binomial thinning operator are presented. These characterizations allow the construction of statistics for testing the Poisson distribution against alternatives belonging to a large family called the LC-class, and against general alternatives. The usefulness and the power of the tests are illustrated with several examples of applications in Biodosimetry.

Suggested Citation

  • Puig, Pedro & Weiß, Christian H., 2020. "Some goodness-of-fit tests for the Poisson distribution with applications in Biodosimetry," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  • Handle: RePEc:eee:csdana:v:144:y:2020:i:c:s0167947319302336
    DOI: 10.1016/j.csda.2019.106878
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947319302336
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2019.106878?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tammy Harris & Joseph M. Hilbe & James W. Hardin, 2014. "Modeling count data with generalized distributions," Stata Journal, StataCorp LP, vol. 14(3), pages 562-579, September.
    2. Hilbe,Joseph M., 2014. "Modeling Count Data," Cambridge Books, Cambridge University Press, number 9781107611252, October.
    3. Pedro Puig & Célestin C. Kokonendji, 2018. "Non†parametric Estimation of the Number of Zeros in Truncated Count Distributions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(2), pages 347-365, June.
    4. José González-Barrios & Federico O’Reilly & Raúl Rueda, 2006. "Goodness of Fit for Discrete Random Variables Using the Conditional Density," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 64(1), pages 77-94, August.
    5. Mònica Pujol & Joan-Francesc Barquinero & Pedro Puig & Roser Puig & María Rosa Caballín & Leonardo Barrios, 2014. "A New Model of Biodosimetry to Integrate Low and High Doses," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-19, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Muhammad Aslam, 2022. "Neutrosophic F-Test for Two Counts of Data from the Poisson Distribution with Application in Climatology," Stats, MDPI, vol. 5(3), pages 1-11, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Jiang & Ou, Guiyan & Liu, Xiaohui & Dong, Ke, 2022. "How does academic education background affect top researchers’ performance? Evidence from the field of artificial intelligence," Journal of Informetrics, Elsevier, vol. 16(2).
    2. Mutz, Rüdiger & Daniel, Hans-Dieter, 2018. "The bibliometric quotient (BQ), or how to measure a researcher’s performance capacity: A Bayesian Poisson Rasch model," Journal of Informetrics, Elsevier, vol. 12(4), pages 1282-1295.
    3. Chiara Bocci & Laura Grassini & Emilia Rocco, 2021. "A multiple inflated negative binomial hurdle regression model: analysis of the Italians’ tourism behaviour during the Great Recession," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1109-1133, October.
    4. Maniezzo, Vittorio & Boschetti, Marco A. & Gutjahr, Walter J., 2021. "Stochastic premarshalling of block stacking warehouses," Omega, Elsevier, vol. 102(C).
    5. Geng, Xi & Xia, Aihua, 2022. "When is the Conway–Maxwell–Poisson distribution infinitely divisible?," Statistics & Probability Letters, Elsevier, vol. 181(C).
    6. Smith, David M. & Faddy, Malcolm J., 2016. "Mean and Variance Modeling of Under- and Overdispersed Count Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i06).
    7. de Rezende, Rafael & Egert, Katharina & Marin, Ignacio & Thompson, Guilherme, 2022. "A white-boxed ISSM approach to estimate uncertainty distributions of Walmart sales," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1460-1467.
    8. Andries, Petra & Hünermund, Paul, 2020. "Firm-level effects of staged investments in innovation: The moderating role of resource availability," Research Policy, Elsevier, vol. 49(7).
    9. Lin, Jun-You, 2017. "Balancing industry collaboration and academic innovation: The contingent role of collaboration-specific attributes," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 216-228.
    10. Di Novi, Cinzia & Leporatti, Lucia & Levaggi, Rosella & Montefiori, Marcello, 2022. "Adherence during COVID-19: The role of aging and socio-economics status in shaping drug utilization," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 1-14.
    11. Hu, Feng & Bijmolt, Tammo H.A. & Huizingh, Eelko K.R.E., 2020. "The impact of innovation contest briefs on the quality of solvers and solutions," Technovation, Elsevier, vol. 90.
    12. Sinclair, Michael & Ghermandi, Andrea & Signorello, Giovanni & Giuffrida, Laura & De Salvo, Maria, 2022. "Valuing Recreation in Italy's Protected Areas Using Spatial Big Data," Ecological Economics, Elsevier, vol. 200(C).
    13. Hilbe, Joseph M., 2015. "The New Statistics with R: An Introduction for Biologists," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(b01).
    14. Corona Francisco & Horrillo Juan de Dios Tena & Wiper Michael Peter, 2017. "On the importance of the probabilistic model in identifying the most decisive games in a tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(1), pages 11-23, March.
    15. Marques, Samuel de França & Pitombo, Cira Souza, 2023. "Local modeling as a solution to the lack of stop-level ridership data," Journal of Transport Geography, Elsevier, vol. 112(C).
    16. Soares, Thiago J. & Torkomian, Ana L.V. & Nagano, Marcelo Seido, 2020. "University regulations, regional development and technology transfer: The case of Brazil," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    17. Molnár, D. László & Hollósné Marosi, Judit, 2015. "Az öregségi nyugdíjasok halandósága. A nyugellátási összeg, a nyugdíjazási életkor és a halandóság összefüggései Magyarországon, 2004-2012 [Mortality of old-age pensioners. Association among the am," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(12), pages 1258-1290.
    18. Escario, José-Julián & Giménez-Nadal, J. Ignacio & Wilkinson, Anna V., 2022. "Predictors of adolescent truancy: The importance of cyberbullying, peer behavior, and parenting style," Children and Youth Services Review, Elsevier, vol. 143(C).
    19. Requena, Miguel & Reher, David Sven, 2023. "Intergenerational transmission of fertility in Spain among cohorts born during the first half of twentieth century," Economics & Human Biology, Elsevier, vol. 50(C).
    20. Mutz, Rüdiger & Bornmann, Lutz & Daniel, Hans-Dieter, 2017. "Are there any frontiers of research performance? Efficiency measurement of funded research projects with the Bayesian stochastic frontier analysis for count data," Journal of Informetrics, Elsevier, vol. 11(3), pages 613-628.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:144:y:2020:i:c:s0167947319302336. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.