Based on the characteristics of the detection likelihood ratio sequence and the recursive relations, a fast algorithm is proposed.
To detect unknown signals, we usually use the generalized likelihood ration test (GLRT).
Both likelihood ratio tests and Bayesian inference are employed to study the phylogeny of Phasianidae.
It is shown that the asymptotic null distribution of the modified likelihood ratio test proposed is derived and found to be relatively simple and easily applied.
In this paper, We present an edge detection algorithm using generalized likelihood ratio.
In this paper, the estimation of parameters for nominal scale population is discussed at first, Then the way of liklihood ratio test is given to judge the problem about the equal of two nominal scale pooulations, besed on the limit distribution of likelihood ratio statistic.
The family of generalized empirical likelihood ratio statistics with moment restrictions, which is a generalization of Baggerly, is investigated.
The limit distributions of estimators and likelihood ratio test are given, the strong consistency of estimators is also proved.
The method can improve the performance of likelihood ratio verification method in mismatch conditions by incorporating robust neighborhood information for a target model.
The maximum likelihood estimators(MLE) of means and standard deviations and the asymptotic distribution of likelihood ratio statistic are given.
Their positive result likelihood ratio(PRLR) was analysed.
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