Fitting Second-Order Parametric Distributions to Data Using Maximum Likelihood Estimation

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Abstract

Second-order random variables, i.e., parametric random variables with uncertain parameters, give risk assessors a way to distinguish and represent both the variability and the uncertainty in an exposure variable. In this manuscript, we explore ways to fit second order random variables to data using maximum likelihood estimation (MLE).

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Authors

David E. Burmaster

Harvard University

Kimberly M. Thompson

Kidney Research UK

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Human and Ecological Risk Assessment An International Journal

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Harvard University

Center for Policy Analysis

Alceon Corporation (United States)

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Burmaster et al. (Wed,) studied this question.

synapsesocial.com/papers/6a205c0677803e985598fc94 — DOI: https://doi.org/10.1080/10807039891284361

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Also consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

An Introduction to the Bootstrap· 1994 · 39,700 citations
Uncertainty and Variability in Human Exposures to Soil Contaminants Through Home‐Grown Food: A Monte Carlo Assessment· 1994 · 89 citations
Statistical Data Analysis in the Computer Age· 1991 · 1,203 citations
Mathematica. A system for doing mathematics by computer· 1988 · 2,820 citations
Human Interindividual Variability in Cancer Risks—Technical and Management Challenges· 1996 · 37 citations

Fitting Second-Order Parametric Distributions to Data Using Maximum Likelihood Estimation

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Abstract

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Authors

Journals

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Institutions

References and Citations

Citation Network

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Discussion

Cite this study

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