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Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods
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| Zeitschriftentitel: | Journal of the Royal Statistical Society Series B: Statistical Methodology |
|---|---|
| Personen und Körperschaften: | , |
| In: | Journal of the Royal Statistical Society Series B: Statistical Methodology, 68, 2006, 5, S. 859-872 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
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Neuhaus, John M. McCulloch, Charles E. Neuhaus, John M. McCulloch, Charles E. |
|---|---|
| author |
Neuhaus, John M. McCulloch, Charles E. |
| spellingShingle |
Neuhaus, John M. McCulloch, Charles E. Journal of the Royal Statistical Society Series B: Statistical Methodology Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods Statistics, Probability and Uncertainty Statistics and Probability |
| author_sort |
neuhaus, john m. |
| spelling |
Neuhaus, John M. McCulloch, Charles E. 1369-7412 1467-9868 Oxford University Press (OUP) Statistics, Probability and Uncertainty Statistics and Probability http://dx.doi.org/10.1111/j.1467-9868.2006.00570.x <jats:title>Summary</jats:title><jats:p>We consider the situation where the random effects in a generalized linear mixed model may be correlated with one of the predictors, which leads to inconsistent estimators. We show that conditional maximum likelihood can eliminate this bias. Conditional likelihood leads naturally to the partitioning of the covariate into between- and within-cluster components and models that include separate terms for these components also eliminate the source of the bias. Another viewpoint that we develop is the idea that many violations of the assumptions (including correlation between the random effects and a covariate) in a generalized linear mixed model may be cast as misspecified mixing distributions. We illustrate the results with two examples and simulations.</jats:p> Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods Journal of the Royal Statistical Society Series B: Statistical Methodology |
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| title |
Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| title_unstemmed |
Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| title_full |
Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| title_fullStr |
Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| title_full_unstemmed |
Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| title_short |
Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
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separating between- and within-cluster covariate effects by using conditional and partitioning methods |
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Statistics, Probability and Uncertainty Statistics and Probability |
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http://dx.doi.org/10.1111/j.1467-9868.2006.00570.x |
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2006 |
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859-872 |
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<jats:title>Summary</jats:title><jats:p>We consider the situation where the random effects in a generalized linear mixed model may be correlated with one of the predictors, which leads to inconsistent estimators. We show that conditional maximum likelihood can eliminate this bias. Conditional likelihood leads naturally to the partitioning of the covariate into between- and within-cluster components and models that include separate terms for these components also eliminate the source of the bias. Another viewpoint that we develop is the idea that many violations of the assumptions (including correlation between the random effects and a covariate) in a generalized linear mixed model may be cast as misspecified mixing distributions. We illustrate the results with two examples and simulations.</jats:p> |
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| description | <jats:title>Summary</jats:title><jats:p>We consider the situation where the random effects in a generalized linear mixed model may be correlated with one of the predictors, which leads to inconsistent estimators. We show that conditional maximum likelihood can eliminate this bias. Conditional likelihood leads naturally to the partitioning of the covariate into between- and within-cluster components and models that include separate terms for these components also eliminate the source of the bias. Another viewpoint that we develop is the idea that many violations of the assumptions (including correlation between the random effects and a covariate) in a generalized linear mixed model may be cast as misspecified mixing distributions. We illustrate the results with two examples and simulations.</jats:p> |
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| spelling | Neuhaus, John M. McCulloch, Charles E. 1369-7412 1467-9868 Oxford University Press (OUP) Statistics, Probability and Uncertainty Statistics and Probability http://dx.doi.org/10.1111/j.1467-9868.2006.00570.x <jats:title>Summary</jats:title><jats:p>We consider the situation where the random effects in a generalized linear mixed model may be correlated with one of the predictors, which leads to inconsistent estimators. We show that conditional maximum likelihood can eliminate this bias. Conditional likelihood leads naturally to the partitioning of the covariate into between- and within-cluster components and models that include separate terms for these components also eliminate the source of the bias. Another viewpoint that we develop is the idea that many violations of the assumptions (including correlation between the random effects and a covariate) in a generalized linear mixed model may be cast as misspecified mixing distributions. We illustrate the results with two examples and simulations.</jats:p> Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods Journal of the Royal Statistical Society Series B: Statistical Methodology |
| spellingShingle | Neuhaus, John M., McCulloch, Charles E., Journal of the Royal Statistical Society Series B: Statistical Methodology, Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods, Statistics, Probability and Uncertainty, Statistics and Probability |
| title | Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| title_full | Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| title_fullStr | Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| title_full_unstemmed | Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| title_short | Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| title_sort | separating between- and within-cluster covariate effects by using conditional and partitioning methods |
| title_unstemmed | Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods |
| topic | Statistics, Probability and Uncertainty, Statistics and Probability |
| url | http://dx.doi.org/10.1111/j.1467-9868.2006.00570.x |