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A locally directionally maximin test for a multidimensional parameter with order-restricted alternatives in the sense of the local power. For locally asymptotically normal experiments we construct an asymptotic
Local Asymptotic Normality of Statistical Experiments in an Inhomogeneous Competing Risks Model under Random Censoring on the RightThe local asymptotic normality property of the likelihood ratio statistic in the competing
risk
A locally directionally maximin test for a multidimensional parameter with order-restricted alternatives in the sense of the local power. For locally asymptotically normal experiments we construct an asymptotic
Local Asymptotic Normality of Statistical Experiments in an Inhomogeneous Competing Risks Model
statistics (LRS), proved the theorem on the locally asymptotic normality of statistical experiment
Local Asymptotic Normality of Family of Distributions from Incomplete ObservationsIn this paper we prove the property of local asymptotic normality of the likelihood ratio
Asymptotic normality of kernel type density estimators for random fields are asymptotically normal if the set of locations of observations become more and more dense in an increasing
Local asymptotic efficiency of a sequential probability ratio test for d-guarantee discrimination of composite hypotheses restrictions β0 and β1. An asymptotic behavior of boundaries A, B and the average observation time are studied
Asymptotic normality of kernel type density estimators for random fields are asymptotically normal if the set of locations of observations become more and more dense in an increasing
Local asymptotic efficiency of a sequential probability ratio test for d-guarantee discrimination of composite hypotheses restrictions β0 and β1. An asymptotic behavior of boundaries A, B and the average observation time are studied
Transfer theorems concerning asymptotic expansions for the distribution functions of statistics based on samples with random sizes that all those statistics that are regarded as asymptotically normal in the classical sense, become
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