Strong Consistency of Maximum Likelihood Estimators in Extreme-Maximum-Value Distribution Model

Chang Ming Yin; Bo Hong Chen; Shuang Hua Liu

doi:10.4028/www.scientific.net/AMM.525.671

Paper Titles

A Continuous Semigroup Approach to the Distributional Stability of Nonlinear Models
p.653

A Study on the Application of Learning Vector Quantization Neural Network in Pattern Classification
p.657

Application of GQJS in Analysis of Bridge Structure
p.661

Based on Vari Combination Forecasting Model for Power Plant Flue Gas Oxygen Contentance-Covariance Method
p.665

Strong Consistency of Maximum Likelihood Estimators in Extreme-Maximum-Value Distribution Model
p.671

Design and Implement of Equipment Resources Management Support System Based on ExtJS Integrated Framework
p.677

What are the Resistances that Hamper the Application of BIM in China?
p.681

Research on Mud Pulse Signal Starting Time Extracting Algorithm in MWD
p.685

Research on the Development Barriers of BIM in China
p.691

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 525Strong Consistency of Maximum Likelihood...

Strong Consistency of Maximum Likelihood Estimators in Extreme-Maximum-Value Distribution Model

Abstract:

For the extreme-maximum-value distribution model, we show that maximum likelihood estimates of regression parameter vector is asymptotically existence and strongly consistent under mild conditions

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 525)

Pages:

671-676

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.525.671

Citation:

Cite this paper

Online since:

February 2014

Authors:

Chang Ming Yin*, Bo Hong Chen, Shuang Hua Liu

Keywords:

Extreme-Maximum-Value-Distribution Model, Maximum Likelihood Estimators, Strong Consistency

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

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

Google Scholar

[2] 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

[3] R. W. M. Wedderburn. Biometrika, Vol. 61(1974), pp.439-447.

Google Scholar

[4] K. Y. Liang, S. L. Zeger. Biometrika, Vol. 73(1986), pp.13-22.

Google Scholar

[5] L. Fahrmeir. Statistic, Vol. 21(1990), pp.487-502.

Google Scholar

[6] L. Yue, X. R. Chen. Science in China Ser A, Vol. 47(2004), pp.882-893.

Google Scholar

[7] J. L. Ding, X. R. Chen. Acta Mathematica Scientia, Ser. A, Vol. 26(2006), pp.168-173(in Chinese).

Google Scholar

[8] Y. C. I. Chang. Statist Probab Lett, Vol. 45(1999), pp.237-246.

Google Scholar

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

Google Scholar

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

Google Scholar

[11] G. Bennett. J. Amer. Statist. Assoc. Vol. 57(1962), pp.33-45.

Google Scholar