The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform
Norman C. Beaulieu - One of the best experts on this subject based on the ideXlab platform.
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an approximate ml estimator for the Location Parameter of the generalized gaussian distribution with p 5
IEEE Signal Processing Letters, 2013Co-Authors: Norman C. BeaulieuAbstract:Estimation of the Location Parameter of the generalized Gaussian distribution with shape Parameter p = 5 is studied and a novel closed-form approximate maximum likelihood estimator is proposed. The new estimator outperforms all other closed-form estimators for p=5 .
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true ml estimator for the Location Parameter of the generalized gaussian distribution with p 4
IEEE Communications Letters, 2013Co-Authors: Norman C. BeaulieuAbstract:Estimation of the Location Parameter of the generalized Gaussian distribution with shape Parameter p=4 is studied and an explicit solution for the maximum likelihood estimator is derived. The Cramer Rao lower bound is derived and the mean square error of the new estimator is compared to it. The new maximum likelihood estimator attains the Cramer Rao lower bound for a moderate number of samples. Simulation results show the explicit maximum likelihood estimator has superior performance compared to the mean estimator and slightly better performance than the moment/Newton-step estimator. The new maximum likelihood estimator has similar computational complexity to the moment/Newton-step estimator.
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An Approximate ML Estimator for the Location Parameter of the Generalized Gaussian Distribution With $p=5$
IEEE Signal Processing Letters, 2013Co-Authors: Norman C. BeaulieuAbstract:Estimation of the Location Parameter of the generalized Gaussian distribution with shape Parameter p = 5 is studied and a novel closed-form approximate maximum likelihood estimator is proposed. The new estimator outperforms all other closed-form estimators for p=5 .
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True ML Estimator for the Location Parameter of the Generalized Gaussian Distribution with p = 4
IEEE Communications Letters, 2013Co-Authors: Norman C. BeaulieuAbstract:Estimation of the Location Parameter of the generalized Gaussian distribution with shape Parameter p=4 is studied and an explicit solution for the maximum likelihood estimator is derived. The Cramér Rao lower bound is derived and the mean square error of the new estimator is compared to it. The new maximum likelihood estimator attains the Cramér Rao lower bound for a moderate number of samples. Simulation results show the explicit maximum likelihood estimator has superior performance compared to the mean estimator and slightly better performance than the moment/Newton-step estimator. The new maximum likelihood estimator has similar computational complexity to the moment/Newton-step estimator.
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Novel Estimator for the Location Parameter of the Generalized Gaussian Distribution
IEEE Communications Letters, 2012Co-Authors: Norman C. BeaulieuAbstract:The estimation of the Location Parameter of the generalized Gaussian distribution with shape Parameter p ≥ 2 is studied and a novel estimator, based on an approximation to the maximum likelihood estimator, is proposed. The new estimator is simple with low complexity and is proved to be unbiased. Numerical results show that the mean square error of the new estimator is much lower than that of the widely used mean estimator. The new estimator has almost identical mean square error as the maximum likelihood estimator.
Francois Vernotte - One of the best experts on this subject based on the ideXlab platform.
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Can We Define a Best Estimator in Simple One-Dimensional Cases? [Lecture Notes]
IEEE Signal Processing Magazine, 2013Co-Authors: Eric Lantz, Francois VernotteAbstract:What is the best estimator for assessing a Parameter of a probability distribution from a small number of measurements? Is the same answer valid for a Location Parameter like the mean as for a scale Parameter like the variance? It is sometimes argued that it is better to use a biased estimator with low dispersion than an unbiased estimator with a higher dispersion. In which cases is this assertion correct? To answer these questions, we will compare, on a simple example, the determination of a Location Parameter and a scale Parameter with three "optimal" estimators: the minimum-variance unbiased estimator, the minimum square error estimator, and the a posteriori mean.
Zhenmin Chen - One of the best experts on this subject based on the ideXlab platform.
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Interval and Point Estimators for the Location Parameter of the Three-Parameter Lognormal Distribution
International Journal of Quality Statistics and Reliability, 2012Co-Authors: Zhenmin Chen, Feng MiaoAbstract:The three-Parameter lognormal distribution is the extension of the two-Parameter lognormal distribution to meet the need of the biological, sociological, and other fields. Numerous research papers have been published for the Parameter estimation problems for the lognormal distributions. The inclusion of the Location Parameter brings in some technical difficulties for the Parameter estimation problems, especially for the interval estimation. This paper proposes a method for constructing exact confidence intervals and exact upper confidence limits for the Location Parameter of the three-Parameter lognormal distribution. The point estimation problem is discussed as well. The performance of the point estimator is compared with the maximum likelihood estimator, which is widely used in practice. Simulation result shows that the proposed method is less biased in estimating the Location Parameter. The large sample size case is discussed in the paper.
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Statistical inference about the Location Parameter of the three-Parameter Weibull distribution
Journal of Statistical Computation and Simulation, 2009Co-Authors: Dongming Chen, Zhenmin ChenAbstract:Exact confidence intervals, confidence limits and point estimators for the Location Parameter μ of the three-Parameter Weibull distributions have been investigated in the literature. One of the purposes of this paper is to find the best selection of i, j and k for the approach given by Chen [Z. Chen, Exact confidence intervals and joint confidence regions for the Parameters of the Weibull distributions, Int. J. Reliab., Qual. Safety Eng. 11 (2004), pp. 133–140.] for constructing an exact confidence interval of the Location Parameter μ. Statistical simulation has been conducted to find the optimal combination. The critical values of the pivotal quantity ω are obtained. The point estimation for the Location Parameter of the three-Parameter Weibull distributions is also discussed. Compared with the commonly used maximum likelihood estimation method, the method introduced in this research provides a simpler, more accurate and more efficient way to estimate the Location Parameter of the three-Parameter Weibull...
Ayman Baklizi - One of the best experts on this subject based on the ideXlab platform.
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estimation of common Location Parameter of two exponential populations based on records
Communications in Statistics-theory and Methods, 2019Co-Authors: Mohd Arshad, Ayman BakliziAbstract:ABSTRACTConsider the problem of estimating the common Location Parameter of two exponential populations using record data when the scale Parameters are unknown. We derive the maximum likelihood estimator (MLE), the modified maximum likelihood estimator (MMLE) and the uniformly minimum variance unbiased estimator (UMVUE) of the common Location Parameter. Further, we derive a general result for inadmissibility of an equivariant estimator under the scaled-squared error loss function. Using this result, we conclude that the MLE and the UMVUE are inadmissible and better estimators are provided. A simulation study is conducted for comparing the performances of various competing estimators.
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shrinkage estimation of p x y in the exponential case with common Location Parameter
Metrika, 2004Co-Authors: Ayman Baklizi, Abed Elqader ElmasriAbstract:We consider the problem of estimating R=P(X>Y) where X and Y have independent exponential distributions with Parameters θ and λ respectively and a common Location Parameter μ. Assuming that there is a prior guess or estimate R 0 , we develop various shrinkage estimators of R that incorporate this prior information. The performance of the new estimators is investigated and compared with the maximum likelihood estimator using Monte Carlo methods. It is found that some of these estimators are very successful in taking advantage of the prior estimate available. Copyright Springer-Verlag 2004
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Shrinkage Estimation of the Common Location Parameter of Several Exponentials
Communications in Statistics - Simulation and Computation, 2004Co-Authors: Ayman BakliziAbstract:Abstract Estimation of the common Location Parameter of several exponentials is considered. Using samples from m independent exponential populations with common Location Parameter θ, and given a prior guess θ0 of θ, several shrinkage estimators have been proposed that incorporate this prior information. We propose shrinkage factors by minimizing the mean squared error or utilizing the P-values obtained from combining certain independent statistics and tests. A simulation study is conducted to investigate the performance of the proposed estimators. It is found that the proposed estimators are effective in taking advantage of the available prior information.
H. Messer - One of the best experts on this subject based on the ideXlab platform.
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A new method for estimating Parameters of a skewed alpha-stable distribution
2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 2000Co-Authors: S. Maymon, J. Friedmann, H. MesserAbstract:Estimating the Parameters of a skewed /spl alpha/-stable distribution calls for estimation of four unknown Parameters of the probability density function (PDF): the Location Parameter, the scale Parameter, the characteristic exponent and the skewness Parameter. We present cumulative distribution function (CDF) based estimators for either the Location Parameter, the skewness Parameter, or the characteristic exponent. The estimators are simple, consistent and their asymptotic performance is analyzed. Of a particular interest is the new estimator for the skewness Parameter which is given in a closed form, as a function of the other Parameters. As such, it can be used for reducing the search dimension when joint Parameter estimation of a skewed stable distribution is called for.
