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New Family Of Estimators For The Loglinear Model Of Quasi-Independence Based On Power-Divergence Measures

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Felipe Ortega, Ángel and Pardo Llorente, Leandro (2007) New Family Of Estimators For The Loglinear Model Of Quasi-Independence Based On Power-Divergence Measures. Journal of Statistical Computation and Simulation, 77 (5). pp. 407-420. ISSN 0094-9655

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Official URL: http://www.tandfonline.com/doi/pdf/10.1080/10629360600890154


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Abstract

We study the minimum power-divergence estimator, introduced and studied by N. Cressie and T. R. C. Read [Multinomial goodness-of-fit tests. J. R. Stat. Soc., Ser. B 46, 440–464 (1984), in the loglinear model of quasi-independence.
A simulation study illustrates that minimum chi-squared estimator and Cressie-Read estimator are good alternatives to the classical maximum-likelihood estimator for this
problem.
The estimator obtained for = 2 is the most robust and efficient estimator among the family of the minimum power estimators.


Item Type:Article
Additional Information:

loglinear model, quasi-independence, maximum likelihood, minimum powerdivergence estimator

Uncontrolled Keywords:Loglinear Model; Quasi-Independence; Maximum Likelihood; Minimum Power-Divergence Estimator;Minimum; Distance; Computer Science, Interdisciplinary Applications; Statistics & Probability
Subjects:Sciences > Mathematics > Differential equations
ID Code:15086
Deposited On:04 May 2012 11:34
Last Modified:19 Feb 2019 11:16

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