A model-based approach for imputing censored data in source apportionment studies

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Recent Approaches to Estimate Associations Between Source-Specific Air Pollution and Health.Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health

P2860

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A model-based approach for imputing censored data in source apportionment studies

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2015 nî lūn-bûn

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2015年論文

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A model-based approach for imputing censored data in source apportionment studies

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A model-based approach for imputing censored data in source apportionment studies

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A model-based approach for imputing censored data in source apportionment studies

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A model-based approach for imputing censored data in source apportionment studies

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A model-based approach for imputing censored data in source apportionment studies

@ast

A model-based approach for imputing censored data in source apportionment studies

@en

P1476

A model-based approach for imputing censored data in source apportionment studies

@en

P2093

Charles H Simpson

http://www.w3.org/2001/XMLSchema#string

Roger D Peng

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P2860

Fine particulate source apportionment using data from the USEPA speciation trends network in Chicago, Illinois: comparison of two source apportionment models

Association of fine particulate matter from different sources with daily mortality in six U.S. cities

Hospital admissions and chemical composition of fine particle air pollution

A Bayesian Multiple Imputation Method for Handling Longitudinal Pesticide Data with Values below the Limit of Detection

Time-series analysis of mortality effects of fine particulate matter components in Detroit and Seattle

Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution

The effects of components of fine particulate air pollution on mortality in california: results from CALFINE

Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values

More than obvious: better methods for interpreting nondetect data.

Fabricating data: how substituting values for nondetects can ruin results, and what can be done about it.

P2888

s10651-015-0319-6

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779-800

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scholarly article

P356

10.1007/S10651-015-0319-6

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P433

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P478

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Jenna R Krall

P577

2015-06-04T00:00:00Z

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26640398

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P932

4667983

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A model-based approach for imputing censored data in source apportionment studies

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P2860

P2860

A model-based approach for imputing censored data in source apportionment studies

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P2888

P304

P31

P356

P433

P478

P50

P577

P698

P932

P356

P1433

P1476

P2093

P2860

P2888

P304

P31

P356

P433

P478

P50

P577

P698

P932