Abstract
Reminding the framework of discrete smoothing using discrete associated kernel methods, binomial kernel with local Bayesian bandwidth selection is presented, for estimating a probability mass function under a Poisson-weighted assumption (Senga-Kiessé et al. Comput Stat 31:189–206, 2016, [11]). Model diagnostics are evoked between three approaches: parametric, nonparametric and semiparametric. Finally, some applications are done on real count datasets of low and high radiation doses in biodosimetry, as alternatives to the parametric approaches in Pujol et al. (PLoS ONE 9(12):e114137, 2014, [9]).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
B. Abdous, C.C. Kokonendji, and T. Senga-Kiessé, “On semiparametric regression for count explanatory variables”, J. Statist. Plann. Inference 142 (2012), 1537–1548.
C.C. Kokonendji, “Over- and underdispersion models”, in “Methods and Applications of Statistics in Clinical Trials”, Vol. 2, Chap. 30 (2014), 506–526.
C.C. Kokonendji, D. Mizère, and N. Balakrishnan, “Connections of the Poisson weight function to overdispersion and underdispersion”, J. Statist. Plann. Inference 138 (2008), 1287–1296.
C.C. Kokonendji and M. Pérez-Casany, “A note on weighted count distributions”, J. Statist. Th. Appl. 11 (2012), 337–352.
C.C. Kokonendji and T. Senga-Kiessé, “Discrete associated kernels method and extensions”, Statistical Methodology 8 (2011), 497–516.
C.C. Kokonendji, T. Senga-Kiessé, and N. Balakrishnan, “Semiparametric estimation for count data through weighted distributions”, J. Statist. Plann. Inference 139 (2009), 3625–3638.
C.C. Kokonendji, T. Senga-Kiessé, and C.G.B. Demétrio, “Appropriate kernel regression on a count explanatory variable and applications”, Adv. Appl. Statist. 12 (2009), 99–125.
C.C. Kokonendji and S.S. Zocchi, “Extensions of discrete triangular distribution and boundary bias in kernel estimation for discrete functions”, Statist. Probab. Lett. 80 (2010), 1655–1662.
M. Pujol, J.F. Barquinero, P. Puig, R. Puig, M.R. Caballin, and L. Barrios, “A new model of biodosimetry to integrate low and high doses”, PLoS ONE 9(12) (2014), e114137.
T. Senga-Kiessé and D. Mizère, “Weighted Poisson and semiparametric kernel models applied for a parasite growth”, Aust. New Zealand J. Statist. 55 (2012), 1–13.
T. Senga-Kiessé, N. Zougab, and C.C. Kokonendji, “Bayesian estimation of bandwidth in semiparametric kernel estimation of unknown probability mass and regression functions of count data”, Comput. Statist. 31 (2016), 189–206.
N. Zougab, S. Adjabi, and C.C. Kokonendji, “Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation”, Comput. Statist. Data Anal. 75 (2014), 28–38.
N. Zougab, S. Adjabi, and C.C. Kokonendji, “Comparison study to bandwidth selection in binomial kernel estimation using Bayesian approaches”, J. Statist. Theor. Pract. 10 (2016), 133–153.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kokonendji, C.C., Zougab, N., Senga-Kiessé, T. (2017). Poisson-Weighted Estimation by Discrete Kernel with Application to Radiation Biodosimetry. In: Ainsbury, E., Calle, M., Cardis, E., Einbeck, J., Gómez, G., Puig, P. (eds) Extended Abstracts Fall 2015. Trends in Mathematics(), vol 7. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-55639-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-55639-0_19
Published:
Publisher Name: Birkhäuser, Cham
Print ISBN: 978-3-319-55638-3
Online ISBN: 978-3-319-55639-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)