Abstract
Let f 0(x) be the exponential density and f γ(x) the translation model. Let (X i) i=1,n be i.i.d. random variables, with density g. We test that g is f 0 against g is a simple mixture, using the LRT statistic. We prove that the LRT diverges to infinity with probability 1/2 and it is equal to 0 with probability 1/2. Therefore, the classical likelihood limiting theory does not hold.
Similar content being viewed by others
References
Bickel, P. and Chernoff, H. (1993). Asymptotic Distribution of the Likelihood Ratio Statistic in a Prototypical Non Regular Problem, Statistics and Probability: A Raghu Raj Bahabur Festschrift, 83–96, Wiley, New York.
Ciuperca, G. (1999). Sur le test de maximum de vraisemblance pour le mélange de populations, Comptes-Rendus de l'Académie des Sciences, Serie I, 328(4), 351–356.
Dacunha-Castelle, D. and Duflo, M. (1990). Probabilités et Statistiques, Tome 1, Problèmes à Temps Fixe, Masson, Paris.
Dacunha-Castelle, D. and Gassiat, E. (1997). Testing in locally conic model and application to mixture models, ESAIM Probab. Statist., 1, 285–317.
Everitt, B. S. and Hand, D. J. (1981). Finite Mixture Distributions, Chapman and Hall, New York.
Ghosh, J. and Sen, P. (1985). On the asymptotic performance of the log-likelihood ratio statistic for the mixture model and related results, Proc. Berkeley Conference in Honor of Jerzy Neyman and Jack Kiefer, Vol. II, 789–806, Wadsworth, Belmont, California.
Hanson, D. L. and Russo, R. P (1983). Some results on increments of the Wiener process with applications to lag sums of i.i.d. random variables, Ann. Probab., 11(3), 609–623.
Hartigan, J. A. (1985). A failure of likelihood ratio asymptotics for normal mixtures, Proc. Berkeley Conference in Honor of Jerzy Neyman and Jack Kiefer, Vol. II, 807–810, Wadsworth, Belmont, California.
Lindsay, B. G. (1995). Mixture models: Theory, geometry and applications, NSF-CBMS Regional Conference Series in Probability and Statistics, 5, IMS, Hayward, California; ASA, Alexandria, Virginia.
McLachlan, G. J. and Basford, K. E. (1988). Mixture Models: Inference and Applications to Clustering, Marcel Dekker, New York.
Redner, R. (1981). Note on the Consistency of the maximum likelihood estimate for nonidentifiable distributions, Ann. Statist., 9, 225–228.
Richardson, S. and Green, P. J. (1997). On Bayesian analysis of mixtures with an unknown number of components, J. Roy. Statist. Soc. Ser. B, 59(4), 731–792.
Roberts, S. J., Husmeier, D., Rezek, I. and Penny, W. (1998). Bayesian approaches to Gaussian mixture modeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 887–906.
Shorack, G. R. and Wellner, J. A. (1986). Empirical Processes with Applications to Statitics, Wiley, New York.
Titterington, D. M., Smith, A. F. M. and Makov, U. E. (1985). Statistical Analysis of Finite Mixture Models, Wiley, New York.
Van der Vaart, A. W. and Wellner, J. A. (1996). Weak Convergence and Empirical Processes, Springer, New York.
Author information
Authors and Affiliations
About this article
Cite this article
Ciuperca, G. Likelihood Ratio Statistic for Exponential Mixtures. Annals of the Institute of Statistical Mathematics 54, 585–594 (2002). https://doi.org/10.1023/A:1022415228062
Issue Date:
DOI: https://doi.org/10.1023/A:1022415228062