Abstract
This chapter presents three estimations of generalized variance (i.e., determinant of covariance matrix) of normal-Poisson models: maximum likelihood (ML) estimator, uniformly minimum variance unbiased (UMVU) estimator, and Bayesian estimator. First, the definition and some properties of normal-Poisson models are established. Then ML, UMVU, and Bayesian estimators for generalized variance are derived. Finally, a simulation study is carried out to assess the performance of the estimators based on their mean square error (MSE).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hassairi, A.: Generalized variance and exponential families. Ann. Stat. 27(1), 374–385 (1999)
Kokonendji, C.C., Pommeret, D.: Estimateurs de la variance généralisée pour des familles exponentielles non gaussiennes. C. R. Acad. Sci. Ser. Math. 332(4), 351–356 (2001)
Shorrock, R.W., Zidek, J.V.: An improved estimator of the generalized variance. Ann. Stat. 4(3), 629–638 (1976)
Boubacar Maïnassara, Y., Kokonendji, C.C.: On normal stable Tweedie models and power generalized variance function of only one component. TEST 23(3), 585–606 (2014)
Casalis, M.: The 2d + 4 simple quadratic natural exponential families on Rd. Ann. Stat. 24(4), 1828–1854 (1996)
G. Letac, Le problem de la classification des familles exponentielles naturelles de ℝd ayant une fonction variance quadratique, in Probability Measures on Groups IX, H. Heyer, Ed. Springer, Berlin, 1989, pp. 192–216.
Kokonendji, C.C., Masmoudi, A.: A characterization of Poisson-Gaussian families by generalized variance. Bernoulli 12(2), 371–379 (2006)
Kokonendji, C.C., Seshadri, V.: On the determinant of the second derivative of a Laplace transform. Ann. Stat. 24(4), 1813–1827 (1996)
Kokonendji, C.C., Pommeret, D.: Comparing UMVU and ML estimators of the generalized variance for natural exponential families. Statistics 41(6), 547–558 (2007)
Kotz, S., Balakrishnan, N., Johnson, N.L.: Continuous Multivariate Distributions. Models and Application, vol. 1, 2nd edn. Wiley, New York (2000)
Kokonendji, C.C., Masmoudi, A.: On the Monge–Ampère equation for characterizing gamma-Gaussian model. Stat. Probab. Lett. 83(7), 1692–1698 (2013)
Gutiérrez, C.E.: The Monge-Ampère Equation. Birkhäuser, Boston (2001). Boston: Imprint: Birkhäuser
Gikhman, I.I., Skorokhod, A.V.: The Theory of Stochastic Processes 2. Springer, New York (2004)
Berger, J.O.: Statistical Decision Theory and Bayesian Analysis, 2nd edn. Springer, New York (1985)
Sultan, R., Ahmad, S.P.: Posterior estimates of Poisson distribution using R software. J. Mod. Appl. Stat. Methods 11(2), 530–535 (2012)
Hogg, R.V.: Introduction to Mathematical Statistics, 7th edn. Pearson, Boston (2013)
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kokonendji, C.C., Nisa, K. (2016). Generalized Variance Estimations of Normal-Poisson Models. In: Chen, K., Ravindran, A. (eds) Forging Connections between Computational Mathematics and Computational Geometry. Springer Proceedings in Mathematics & Statistics, vol 124. Springer, Cham. https://doi.org/10.5176/2251-1911_CMCGS14.29_21
Download citation
DOI: https://doi.org/10.5176/2251-1911_CMCGS14.29_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16138-9
Online ISBN: 978-3-319-16139-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)