Estimation of parameters in a network reliability model with spatial dependence

Ian Hepburn Dinwoodie

ESAIM: Probability and Statistics (2005)

  • Volume: 9, page 241-253
  • ISSN: 1292-8100

Abstract

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An iterative method based on a fixed-point property is proposed for finding maximum likelihood estimators for parameters in a model of network reliability with spatial dependence. The method is shown to converge at a geometric rate under natural conditions on data.

How to cite

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Dinwoodie, Ian Hepburn. "Estimation of parameters in a network reliability model with spatial dependence." ESAIM: Probability and Statistics 9 (2005): 241-253. <http://eudml.org/doc/245601>.

@article{Dinwoodie2005,
abstract = {An iterative method based on a fixed-point property is proposed for finding maximum likelihood estimators for parameters in a model of network reliability with spatial dependence. The method is shown to converge at a geometric rate under natural conditions on data.},
author = {Dinwoodie, Ian Hepburn},
journal = {ESAIM: Probability and Statistics},
keywords = {Curie-Weiss; EM-algorithm; iterative proportional scaling; maximum likelihood; network tomography},
language = {eng},
pages = {241-253},
publisher = {EDP-Sciences},
title = {Estimation of parameters in a network reliability model with spatial dependence},
url = {http://eudml.org/doc/245601},
volume = {9},
year = {2005},
}

TY - JOUR
AU - Dinwoodie, Ian Hepburn
TI - Estimation of parameters in a network reliability model with spatial dependence
JO - ESAIM: Probability and Statistics
PY - 2005
PB - EDP-Sciences
VL - 9
SP - 241
EP - 253
AB - An iterative method based on a fixed-point property is proposed for finding maximum likelihood estimators for parameters in a model of network reliability with spatial dependence. The method is shown to converge at a geometric rate under natural conditions on data.
LA - eng
KW - Curie-Weiss; EM-algorithm; iterative proportional scaling; maximum likelihood; network tomography
UR - http://eudml.org/doc/245601
ER -

References

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  10. [10] G. Liang and B. Yu, Maximum pseudo-likelihood estimation in network tomography. IEEE Trans. Signal Process. 51 (2003) 2043–2053. 
  11. [11] M. Marcus and H. Minc, A Survey of Matrix Theory and Matrix Inequalities. Allyn and Bacon, Boston (1964). Zbl0126.02404MR162808
  12. [12] P. Parrilo and B. Sturmfels, Minimizing polynomial functions. http://xyz.lanl.gov/abs/math.OC/0103170 (2002). Zbl1099.13516MR1995016
  13. [13] Y. Tsang, M. Coates and R. Nowak, Passive network tomography using EM algorithms. Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, Utah 3 (May 2001) 1469–1472. 
  14. [14] C.F. Jeff Wu, On the convergence of the EM algorithm. Ann. Statist. 11 (1983) 95–103. Zbl0517.62035
  15. [15] B. Xi, G. Michailidis and V.N. Nair, Estimating network internal losses using a new class of probing experiments. University of Michigan Department of Statistics Technical Report 397 (2003). 

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