Strong Consistency of Maximum Likelihood Estimators in Sequential-Cumulative Logit Model

Chang Ming Yin; Xiao Jie Li; Dan Fu

doi:10.4028/www.scientific.net/AMM.742.445

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 742Strong Consistency of Maximum Likelihood...

Strong Consistency of Maximum Likelihood Estimators in Sequential-Cumulative Logit Model

Abstract:

In this article, for the sequential-cumulative logit model, we show that maximum likelihood estimates of regression parameter vector is asymptotically existence and strongly consistent under mild conditions

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Periodical:

Applied Mechanics and Materials (Volume 742)

Pages:

445-448

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.742.445

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Online since:

March 2015

Authors:

Chang Ming Yin, Xiao Jie Li, Dan Fu

Keywords:

Maximum Likelihood Estimators, Sequential-Cumulative Logit Model, Strong Consistency

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References

[1] L. Fahrmeir, H. Kaufmann. Statist Hefte, Vol. 27(1986), pp.179-205.

Google Scholar

[2] L. Fahrmeir, H. Kanfmann. Ann Statist, Vol. 13(1985), pp.342-368.

Google Scholar

[3] C.M. Yin, L.C. Zhao. Science in China, Ser. A. Vol. 48(2005), pp.1009-1014.

Google Scholar

[4] C. M. Yin, L. C. Zhao, C. D. Wei. Science in China Ser A, Vol. 48(2006), pp.145-157.

Google Scholar

[5] Q.B. Gao, J.G. Lin C.H. Zhu Y.H. Wu. Statistics. Vol. 46(2012), pp.833-846.

Google Scholar

[6] C.M. Yin, Z.F. Wang, H. Zhang. Statist Probab Lett, Vol. 85(2014), pp.135-143.

Google Scholar

[7] C. Mehta,N. Patel, A. TSiatis. Biometrics. Vol. 40(1984), pp.819-825.

Google Scholar

[8] G. Tutz. Biometrical Journal. Vol. 31(1989), pp.259-272.

Google Scholar

[9] L. Fahrmeir, G. Tutz. Multivariate statistcal modelling based on generalized linear models, pp.63-95, New York: Springer-Verlag(1994).

DOI: 10.1007/978-1-4899-0010-4_3

Google Scholar