The Experts below are selected from a list of 24585 Experts worldwide ranked by ideXlab platform
Jabbari M Nooghabi - One of the best experts on this subject based on the ideXlab platform.
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efficient estimation of the parameters of the Pareto Distribution in the presence of outliers
Communications for Statistical Applications and Methods, 2011Co-Authors: U J Dixit, Jabbari M NooghabiAbstract:The moment(MM) and least squares(LS) estimations of the parameters are derived for the Pareto Distribution in the presence of outliers. Further, we have derived a mixture method(MIX) of estimations with MM and LS that shows that the MIX is more efficient. In the final section we have given an example of actual data from a medical insurance company.
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efficient estimation in the Pareto Distribution with the presence of outliers
Statistical Methodology, 2011Co-Authors: U J Dixit, Jabbari M NooghabiAbstract:Abstract The maximum likelihood (ML) and uniformly minimum variance unbiased estimators (UMVUE) of the probability density function (pdf), cumulative Distribution function (cdf) and r th moment are derived for the Pareto Distribution in the presence of outliers. It has been shown that MLE of pdf and cdf are better than their UMVUEs. At the end, these methods are illustrated with the help of real data from an insurance company.
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efficient estimation in the Pareto Distribution
Statistical Methodology, 2010Co-Authors: U J Dixit, Jabbari M NooghabiAbstract:Abstract The maximum likelihood estimation (MLE) of the probability density function (pdf) and cumulative Distribution function (CDF) are derived for the Pareto Distribution. It has been shown that MLEs are more efficient than uniform minimum variance unbiased estimators of pdf and CDF.
Tachen Liang - One of the best experts on this subject based on the ideXlab platform.
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convergence rates for empirical bayes estimation of the scale parameter in a Pareto Distribution
Computational Statistics & Data Analysis, 1993Co-Authors: Tachen LiangAbstract:Abstract Let f(χ∣θ) = αθ α χ α+1 I (θ,∞) (χ) be the pdf of a Pareto Distribution with known shape scale parameter α > 0 and unknown scale parameter θ. We study the problem of estimating the scale parameter θ under a squared-error loss through the nonparametric empirical Bayes approach. An empirical Bayes estimator is proposed and the corresponding asymptotic optimality is also investigated. It is shown that under certain weak conditions the proposed empirical Bayes estimator is asymptotically optimal and the associated rate of convergence is of order O(n − 2 3 ) .
Anna K Panorska - One of the best experts on this subject based on the ideXlab platform.
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parameter estimation for the truncated Pareto Distribution
Journal of the American Statistical Association, 2006Co-Authors: Inmaculada Aban, Mark M Meerschaert, Anna K PanorskaAbstract:The Pareto Distribution is a simple model for nonnegative data with a power law probability tail. In many practical applications, there is a natural upper bound that truncates the probability tail. This article derives estimators for the truncated Pareto Distribution, investigates their properties, and illustrates a way to check for fit. These methods are illustrated with applications from finance, hydrology, and atmospheric science.
U J Dixit - One of the best experts on this subject based on the ideXlab platform.
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efficient estimation of the parameters of the Pareto Distribution in the presence of outliers
Communications for Statistical Applications and Methods, 2011Co-Authors: U J Dixit, Jabbari M NooghabiAbstract:The moment(MM) and least squares(LS) estimations of the parameters are derived for the Pareto Distribution in the presence of outliers. Further, we have derived a mixture method(MIX) of estimations with MM and LS that shows that the MIX is more efficient. In the final section we have given an example of actual data from a medical insurance company.
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efficient estimation in the Pareto Distribution with the presence of outliers
Statistical Methodology, 2011Co-Authors: U J Dixit, Jabbari M NooghabiAbstract:Abstract The maximum likelihood (ML) and uniformly minimum variance unbiased estimators (UMVUE) of the probability density function (pdf), cumulative Distribution function (cdf) and r th moment are derived for the Pareto Distribution in the presence of outliers. It has been shown that MLE of pdf and cdf are better than their UMVUEs. At the end, these methods are illustrated with the help of real data from an insurance company.
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efficient estimation in the Pareto Distribution
Statistical Methodology, 2010Co-Authors: U J Dixit, Jabbari M NooghabiAbstract:Abstract The maximum likelihood estimation (MLE) of the probability density function (pdf) and cumulative Distribution function (CDF) are derived for the Pareto Distribution. It has been shown that MLEs are more efficient than uniform minimum variance unbiased estimators of pdf and CDF.
Inmaculada Aban - One of the best experts on this subject based on the ideXlab platform.
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parameter estimation for the truncated Pareto Distribution
Journal of the American Statistical Association, 2006Co-Authors: Inmaculada Aban, Mark M Meerschaert, Anna K PanorskaAbstract:The Pareto Distribution is a simple model for nonnegative data with a power law probability tail. In many practical applications, there is a natural upper bound that truncates the probability tail. This article derives estimators for the truncated Pareto Distribution, investigates their properties, and illustrates a way to check for fit. These methods are illustrated with applications from finance, hydrology, and atmospheric science.
