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Local model uncertainty and incomplete-data bias
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UNSPECIFIED (2005) Local model uncertainty and incomplete-data bias. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 67 (Part 4). pp. 459-495. ISSN 1369-7412.
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Abstract
Problems of the analysis of data with incomplete observations are all too familiar in statistics. They are doubly difficult if we are also uncertain about the choice of model. We propose a general formulation for the discussion of such problems and develop approximations to the resulting bias of maximum likelihood estimates on the assumption that model departures are small. Loss of efficiency in parameter estimation due to incompleteness in the data has a dual interpretation: the increase in variance when an assumed model is correct; the bias in estimation when the model is incorrect. Examples include non-ignorable missing data, hidden confounders in observational studies and publication bias in meta-analysis. Doubling variances before calculating confidence intervals or test statistics is suggested as a crude way of addressing the possibility of undetectably small departures from the model. The problem of assessing the risk of lung cancer from passive smoking is used as a motivating example.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Journal or Publication Title: | JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY | ||||
Publisher: | BLACKWELL PUBLISHING | ||||
ISSN: | 1369-7412 | ||||
Official Date: | September 2005 | ||||
Dates: |
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Volume: | 67 | ||||
Number: | Part 4 | ||||
Number of Pages: | 37 | ||||
Page Range: | pp. 459-495 | ||||
Publication Status: | Published |
Data sourced from Thomson Reuters' Web of Knowledge
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