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1997•Calcutta Statistical Association BulletinRequires access

Estimation of the Pareto Parameter Under Entropy Loss

Ahmad Parsian, N. Sanjari Farsipour

Open publisher page 3 citations

Abstract

Estimation of the Pareto parameter under the entropy loss function is considered on the basis of a random sample from the two-parameter form of the Pareto distribution in two cases, i. e. when the cut-off parameter is known and is unknown. When the cut-off parameter k is known, it is shown that in the restricted class (1.3), C n-1 is UMRUE and is admissible. When k is unknown, we showed that in the restricted class (1.4), d n-2 is UMRUE but is inadmissible. Estimators dominating d n-2 are provided.

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What this paper is about

Estimation of the Pareto parameter under the entropy loss function is considered on the basis of a random sample from the two-parameter form of the Pareto distribution in two cases, i. e. when the cut-off parameter is known and is unknown. When the cut-off parameter k is known, it is shown that in the restricted class (1.3), C n-1 is UMRUE and is admissible. When k is unknown, we showed that in the restricted class (1.4), d n-2 is UMRUE but is inadmissible. Estimators dominating d n-2 are provided.

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Available abstract

Estimation of the Pareto parameter under the entropy loss function is considered on the basis of a random sample from the two-parameter form of the Pareto distribution in two cases, i. e. when the cut-off parameter is known and is unknown. When the cut-off parameter k is known, it is shown that in the restricted class (1.3), C n-1 is UMRUE and is admissible. When k is unknown, we showed that in the restricted class (1.4), d n-2 is UMRUE but is inadmissible. Estimators dominating d n-2 are provided.

Key concepts: Lomax distribution, Mathematics, Pareto interpolation, Pareto distribution, Pareto principle, Estimator, Estimation theory, Class (philosophy)

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