Journal of the Iranian Statistical Society
پژوهشنامه انجمن آمار ایران
JIRSS
Basic Sciences
http://jirss.irstat.ir
1
admin
1726-4057
2538-189X
8
10.29252/jirss
14
1726-4057
13
en
jalali
1392
7
1
gregorian
2013
10
1
12
2
online
1
fulltext
en
Asymptotic Efficiencies of the MLE Based on Bivariate Record Values from Bivariate Normal Distribution
Abstract. Maximum likelihood (ML) estimation based on bivariate record data is considered as the general inference problem. Assume that the process of observing k records is repeated m times, independently. The asymptotic properties including consistency and asymptotic normality of the Maximum Likelihood (ML) estimates of parameters of the underlying distribution is then established, when m is large enough. The bivariate normal distribution is considered as an highly applicable example in order to estimate the parameter θ = (μ1, σ1, μ2, σ2) by ML method of estimation based on mk bivariate record data. Asymptotic
variances of the ML estimators are calculated by deriving the Fisher information matrix about θ contained in the vector of the first k bivariate record data. As another application, we concerned the problem of “breaking boards” of Glick (1978, Amer. Math. Monthly, 85, 2-26) by considering three different sampling schemes of breaking boards and we computed the relative asymptotic efficiencies of ML estimators based on these three types of data.
Additivity, bivariate distribution, Fisher information matrix, inverse sampling, Jensen’s inequality.
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252
http://jirss.irstat.ir/browse.php?a_code=A-11-1-106&slc_lang=en&sid=1
Morteza
Amini
morteza.amini@ut.ac.ir
`1003194753284600608`

1003194753284600608
Yes
Jafar
Ahmadi
ahmadi-j@um.ac.ir
`1003194753284600609`

1003194753284600609
No