1979TechnometricsRequires access

A Probability Distribution and its Uses in Fitting Data

John S. Ramberg, Edward J. Dudewicz, Pandu R. Tadikamalla, Edward F. Mykytka

Open publisher page 411 citations

Abstract

A four-parameter probability distribution, which includes a wide variety of curve shapes, is presented. Because of the flexibility, generality, and simplicity of the distribution, it is useful in the representation of data when the underlying model is unknown. A table based on the first four moments, which simplifies parameter estimation, is given. Further important applications of the distribution include the modeling and subsequent generation of random variates for simulation studies and Monte Carlo sampling studies of the robustness of statistical procedures.

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What this paper is about

A four-parameter probability distribution, which includes a wide variety of curve shapes, is presented. Because of the flexibility, generality, and simplicity of the distribution, it is useful in the representation of data when the underlying model is unknown. A table based on the first four moments, which simplifies parameter estimation, is given. Further important applications of the distribution include the modeling and subsequent generation of random variates for simulation studies and Monte Carlo sampling studies of the robustness of statistical procedures.

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OpenAlex reports 411 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

A four-parameter probability distribution, which includes a wide variety of curve shapes, is presented. Because of the flexibility, generality, and simplicity of the distribution, it is useful in the representation of data when the underlying model is unknown. A table based on the first four moments, which simplifies parameter estimation, is given. Further important applications of the distribution include the modeling and subsequent generation of random variates for simulation studies and Monte Carlo sampling studies of the robustness of statistical procedures.

Key concepts: Monte Carlo method, Computer science, Distribution fitting, Probability distribution, Robustness (evolution), Sampling distribution, Generality, Algorithm

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