(Q28387016)

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A distribution-based multiple imputation method for handling bivariate pesticide data with values below the limit of detection

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A distribution-based multiple imputation method for handling bivariate pesticide data with values below the limit of detection (English)

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Joseph G Grzywacz

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Thomas A. Arcury

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Thomas A Arcury

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Sara A Quandt

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Sara A Quandt

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Haiying Chen

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English

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publication date

March 2011

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published in

Environmental Health Perspectives

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volume

119

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issue

3

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page(s)

351-6

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cites work

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20 March 2017

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20 March 2017

The limitations due to exposure detection limits for regression models

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More than obvious: better methods for interpreting nondetect data

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Handling of dioxin measurement data in the presence of non-detectable values: overview of available methods and their application in the Seveso chloracne study

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How to estimate moments and quantiles of environmental data sets with non-detected observations? A case study on volatile organic compounds in marine water samples

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Summing nondetects: incorporating low-level contaminants in risk assessment.

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27 November 2018

Correlating two viral load assays with known detection limits

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27 November 2018

Correlation coefficient estimation involving a left censored laboratory assay variable

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A mixture model for occupational exposure mean testing with a limit of detection

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27 November 2018

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PubMed publication ID

21097385

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ResearchGate publication ID

49628579

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(Q28387016)

A distribution-based multiple imputation method for handling bivariate pesticide data with values below the limit of detection

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