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The performance of finding the upper bound of eigenvalues (UBE) is affected by the quality of correlation matrix estimation. In this paper, a new quantile-based maximum likelihood mean estimator is proposed to improve the mean estimation on sparse data condition so as to obtain a more reliable correlation matrix estimate from observed samples. This in turn improves the UBE finding. The study is specially...
The mean value estimation for the output quantity of combined random variables is one of the major issues in measurement. In this paper, a new quantile-based maximum likelihood estimation (QMLE) method for mean value estimation is proposed. It fuses the concept of both empirical and symmetric quantile to incorporate the order statistics into the QMLE. Unlike Sample mean derived basing only on the...
Uncertainty measurement and expression can be considered in different ways, but by using coverage interval is the most popular method if considering the bounding information. In this paper, we derive the theoretical probability distribution function of coverage interval and verify that its accuracy meets the best of fit. We illustrate that it may help the decision of the curve shape for the general...
Robust parameters estimation of sparse data is generally applied to the test cases of time-consuming or high cost data collection. This study concerns with the problem in small sample size which is often encountered in the client data processing for speaker verification. We found that there always exists a coverage mismatch problem between the samples and its population in terms of probability density...
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