Logistic Regression Models for Binary Panel Data with Attrition
We discuss ways of analysing panel data when the response is binary and there is attrition or drop-out. In general, informative or non-ignorable drop-out models are non-identifiable and arbitrary constraints on the drop-out model must be imposed before carrying out a statistical analysis. The proble...
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| Veröffentlicht in: | Journal of the Royal Statistical Society. Series A, Statistics in society Statistics in society, 1996-01, Vol.159 (2), p.249-263 |
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| container_end_page | 263 |
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| container_issue | 2 |
| container_start_page | 249 |
| container_title | Journal of the Royal Statistical Society. Series A, Statistics in society |
| container_volume | 159 |
| creator | Fitzmaurice, Garrett M. Heath, Anthony F. Clifford, Peter |
| description | We discuss ways of analysing panel data when the response is binary and there is attrition or drop-out. In general, informative or non-ignorable drop-out models are non-identifiable and arbitrary constraints on the drop-out model must be imposed before carrying out a statistical analysis. The problem is particularly acute when predictors as well as response variables are lost by attrition. We describe a likelihood-based method for dealing with the drop-out process in this difficult case and show how the effect of non-identifiability can be reduced by importing additional data from a cross-sectional survey of the same population. The methods are primarily motivated by data from the 1987-92 British Election Panel Study and the 1992 British Election Study. |
| doi_str_mv | 10.2307/2983172 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0964-1998 |
| ispartof | Journal of the Royal Statistical Society. Series A, Statistics in society, 1996-01, Vol.159 (2), p.249-263 |
| issn | 0964-1998 1467-985X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_38822168 |
| source | JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing; Oxford University Press Journals All Titles (1996-Current) |
| subjects | Cohort studies Conservatism Diseases Elections em algorithm Exact sciences and technology Forecasts Health Identifiability Linear inference, regression Logistic regression longitudinal data marginal models Mathematics missing data Modeling Parametric models Probability and statistics Regression analysis Sciences and techniques of general use Statistical analysis Statistics Unemployment Voting Voting behavior |
| title | Logistic Regression Models for Binary Panel Data with Attrition |
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