Wavelet threshold based on Stein's unbiased risk estimators of restricted location parameter in multivariate normal
Hamid Karamikabir, Mahmoud Afshari, Fazlollah Lak
Abstract
In this paper, the problem of estimating the mean vector under non-negative constraints on location vector of the multivariate normal distribution is investigated. The value of the wavelet threshold based on Stein's unbiased risk estimators is calculated for the shrinkage estimator in restricted parameter space. We suppose that covariance matrix is unknown and we find the dominant class of shrinkage estimators under Balance loss function. The performance evaluation of the proposed class of estimators is checked through a simulation study by using risk and average mean square error values.
Topics & Concepts
MathematicsEstimatorStatisticsMultivariate statisticsShrinkage estimatorMultivariate normal distributionMean squared errorBias of an estimatorStein's unbiased risk estimateWaveletJames–Stein estimatorMinimum-variance unbiased estimatorApplied mathematicsComputer scienceArtificial intelligenceImage and Signal Denoising MethodsFinancial Risk and Volatility ModelingAdvanced Statistical Methods and Models