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The covariate-adjusted frequency plot

The covariate-adjusted frequency plot
The covariate-adjusted frequency plot
Count data arise in numerous fields of interest. Analysis of these data frequently require distributional assumptions. Although the graphical display of a fitted model is straightforward in the univariate scenario, this becomes more complex if covariate information needs to be included into the model. Stratification is one way to proceed, but has its limitations if the covariate has many levels or the number of covariates is large. The article suggests a marginal method which works even in the case that all possible covariate combinations are different (i.e. no covariate combination occurs more than once). For each covariate combination the fitted model value is computed and then summed over the entire data set. The technique is quite general and works with all count distributional models as well as with all forms of covariate modelling. The article provides illustrations of the method for various situations and also shows that the proposed estimator as well as the empirical count frequency are consistent with respect to the same parameter.
0962-2802
1-15
Holling, Heinz
88d46f56-77ca-4d0e-b035-a51aff735435
Böhning, Walailuck
e41681ae-1c18-42f9-96d2-e725d47dbeec
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Formann, Anton K.
1d7e8ce2-ef06-48ab-82f6-7a2731e4b573
Holling, Heinz
88d46f56-77ca-4d0e-b035-a51aff735435
Böhning, Walailuck
e41681ae-1c18-42f9-96d2-e725d47dbeec
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Formann, Anton K.
1d7e8ce2-ef06-48ab-82f6-7a2731e4b573

Holling, Heinz, Böhning, Walailuck, Böhning, Dankmar and Formann, Anton K. (2013) The covariate-adjusted frequency plot. Statistical Methods in Medical Research, 1-15. (doi:10.1177/0962280212473386).

Record type: Article

Abstract

Count data arise in numerous fields of interest. Analysis of these data frequently require distributional assumptions. Although the graphical display of a fitted model is straightforward in the univariate scenario, this becomes more complex if covariate information needs to be included into the model. Stratification is one way to proceed, but has its limitations if the covariate has many levels or the number of covariates is large. The article suggests a marginal method which works even in the case that all possible covariate combinations are different (i.e. no covariate combination occurs more than once). For each covariate combination the fitted model value is computed and then summed over the entire data set. The technique is quite general and works with all count distributional models as well as with all forms of covariate modelling. The article provides illustrations of the method for various situations and also shows that the proposed estimator as well as the empirical count frequency are consistent with respect to the same parameter.

Full text not available from this repository.

More information

Published date: 1 February 2013
Organisations: Statistics, Statistical Sciences Research Institute, Primary Care & Population Sciences

Identifiers

Local EPrints ID: 350657
URI: https://eprints.soton.ac.uk/id/eprint/350657
ISSN: 0962-2802
PURE UUID: d15567d6-cba8-4b60-9ce9-329d35bcfc1e
ORCID for Dankmar Böhning: ORCID iD orcid.org/0000-0003-0638-7106

Catalogue record

Date deposited: 04 Apr 2013 11:17
Last modified: 24 Sep 2019 00:39

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